
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive


import pandas as pd
from warnings import filterwarnings
filterwarnings('ignore')
pd.set_option("display.max_columns",None)
pd.set_option("display.max_rows",None)

A = pd.read_csv("/content/drive/MyDrive/python/Attrition_Project/Trial 01/HR-Employee-Attrition-Table 1.csv")
A


A.isna().sum()

Attrition                   0
Age                         0
BusinessTravel              0
DailyRate                   0
Department                  0
DistanceFromHome            0
Education                   0
EducationField              0
EmployeeCount               0
EmployeeNumber              0
EnvironmentSatisfaction     0
Gender                      0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobRole                     0
JobSatisfaction             0
MaritalStatus               0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
Over18                      0
OverTime                    0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StandardHours               0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
YearsWithCurrManager        0
dtype: int64


def replacer(df):
    T=pd.DataFrame(df.isna().sum(), columns=['misval'])
    Q = T[T.misval > 0]
    for i in Q.index:
        if df[i].dtypes == "object":
            mode = df[i].mode()[0]
            df[i]=df[i].fillna(mode)
        else:
            mean = round(df[i].mean(), 2)
            df[i] = df[i].fillna(mean)


A['Department'].mode()[0]

'Research & Development'


A['Age'].mean()

36.923809523809524


replacer(A)

A


def preprocessing(X):
    import pandas as pd
    cat = []
    con = []
    for i in X.columns:
        if(X[i].dtypes =='object'):
            cat.append(i)
        else:
            con.append(i)

    X1 = pd.get_dummies(X[cat])
    from sklearn.preprocessing import StandardScaler
    ss = StandardScaler()
    X2 = pd.DataFrame(ss.fit_transform(X[con]),columns=con)

    X3 = X2.join(X1)
    return X3


for i in A.columns:
    print(i, len(A[i].unique()))

Attrition 2
Age 43
BusinessTravel 3
DailyRate 886
Department 3
DistanceFromHome 29
Education 5
EducationField 6
EmployeeCount 1
EmployeeNumber 1470
EnvironmentSatisfaction 4
Gender 2
HourlyRate 71
JobInvolvement 4
JobLevel 5
JobRole 9
JobSatisfaction 4
MaritalStatus 3
MonthlyIncome 1349
MonthlyRate 1427
NumCompaniesWorked 10
Over18 1
OverTime 2
PercentSalaryHike 15
PerformanceRating 2
RelationshipSatisfaction 4
StandardHours 1
StockOptionLevel 4
TotalWorkingYears 40
TrainingTimesLastYear 7
WorkLifeBalance 4
YearsAtCompany 37
YearsInCurrentRole 19
YearsSinceLastPromotion 16
YearsWithCurrManager 18


A.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Attrition                 1470 non-null   int64 
 1   Age                       1470 non-null   int64 
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(27), object(8)
memory usage: 402.1+ KB


import seaborn as sns
import matplotlib.pyplot as plt
x = 1
plt.figure(figsize=(40,40))
for i in A.columns:
    if A[i].dtypes == 'object':
        plt.subplot(7,5,x)
        sns.countplot(A[i])
        x = x + 1
    else:
        plt.subplot(7,5,x)
        sns.distplot(A[i])
        x = x+1


#sns.pairplot(A)


A.columns

Index(['Attrition', 'Age', 'BusinessTravel', 'DailyRate', 'Department',
       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')


X = A.drop(['EmployeeNumber', "MonthlyRate",'Over18', 'StandardHours', 'EmployeeCount'], axis=1)
Y = A[["MonthlyRate"]]


import seaborn as sns
import matplotlib.pyplot as plt
x = 1
plt.figure(figsize=(30,30))
for i in X.columns:
    if X[i].dtypes == 'object':
        plt.subplot(7,5,x)
        sns.boxplot(X[i],Y['MonthlyRate'])
        x = x + 1
    else:
        plt.subplot(7,5,x)
        sns.scatterplot(X[i], Y['MonthlyRate'])
        x=x+1


X = preprocessing(X)

X


X1 = X 
from sklearn.model_selection import train_test_split
xtrain, xtest,ytrain, ytest = train_test_split(X1, Y, test_size = 0.2, random_state=21)
from statsmodels.api import OLS, add_constant
xconst = add_constant(xtrain)
ols = OLS(ytrain, xconst)
model = ols.fit()
model.summary()


model.pvalues.sort_values()

EnvironmentSatisfaction              0.023049
DistanceFromHome                     0.111236
Education                            0.219098
JobInvolvement                       0.270538
MonthlyIncome                        0.274136
Age                                  0.328344
Gender_Female                        0.363495
Gender_Male                          0.363495
JobRole_Research Scientist           0.363495
EducationField_Other                 0.363495
Department_Research & Development    0.363495
JobRole_Laboratory Technician        0.363495
JobRole_Healthcare Representative    0.363495
Department_Sales                     0.363495
OverTime_Yes                         0.363495
OverTime_No                          0.363495
JobRole_Human Resources              0.363495
JobRole_Manufacturing Director       0.363495
Department_Human Resources           0.363495
EducationField_Technical Degree      0.363495
BusinessTravel_Non-Travel            0.363495
EducationField_Medical               0.363495
MaritalStatus_Married                0.363495
BusinessTravel_Travel_Rarely         0.363495
BusinessTravel_Travel_Frequently     0.363495
EducationField_Marketing             0.363495
MaritalStatus_Single                 0.363495
EducationField_Life Sciences         0.363495
MaritalStatus_Divorced               0.363495
JobRole_Sales Representative         0.363495
const                                0.363495
JobRole_Sales Executive              0.363495
JobRole_Research Director            0.363495
JobRole_Manager                      0.363495
EducationField_Human Resources       0.363495
YearsWithCurrManager                 0.370065
DailyRate                            0.385079
YearsAtCompany                       0.423034
WorkLifeBalance                      0.467837
StockOptionLevel                     0.505233
YearsSinceLastPromotion              0.561365
JobLevel                             0.611944
NumCompaniesWorked                   0.787359
PerformanceRating                    0.807527
Attrition                            0.819023
JobSatisfaction                      0.869212
TrainingTimesLastYear                0.877498
YearsInCurrentRole                   0.896551
HourlyRate                           0.915382
TotalWorkingYears                    0.919455
RelationshipSatisfaction             0.949316
PercentSalaryHike                    0.950537
dtype: float64


from re import M
q = pd.DataFrame(model.pvalues, columns = ["Pvalues"])
m = []
for i in q.Pvalues:
  m.append(round(i,3))

q.Pvalues = m
drpd_clms = q[q.Pvalues > 0.9].index
drpd_clms

Index(['HourlyRate', 'PercentSalaryHike', 'RelationshipSatisfaction',
       'TotalWorkingYears'],
      dtype='object')


X1 = X1.drop(drpd_clms, axis = 1)
from sklearn.model_selection import train_test_split
xtrain, xtest,ytrain, ytest = train_test_split(X1, Y, test_size = 0.2, random_state=21)
from statsmodels.api import OLS, add_constant
xconst = add_constant(xtrain)
ols = OLS(ytrain, xconst)
model = ols.fit()
model.summary()


q = pd.DataFrame(model.pvalues, columns = ["Pvalues"])
m = []
for i in q.Pvalues:
  m.append(round(i,3))

q.Pvalues = m
drpd_clms = q[q.Pvalues > 0.8].index
drpd_clms

Index(['JobSatisfaction', 'TrainingTimesLastYear', 'YearsInCurrentRole'], dtype='object')


X1 = X1.drop(drpd_clms, axis = 1)
from sklearn.model_selection import train_test_split
xtrain, xtest,ytrain, ytest = train_test_split(X1, Y, test_size = 0.2, random_state=21)
from statsmodels.api import OLS, add_constant
xconst = add_constant(xtrain)
ols = OLS(ytrain, xconst)
model = ols.fit()
model.summary()


from re import M
q = pd.DataFrame(model.pvalues, columns = ["Pvalues"])
m = []
for i in q.Pvalues:
  m.append(round(i,3))

q.Pvalues = m
drpd_clms = q[q.Pvalues > 0.7].index
drpd_clms

Index(['Attrition', 'NumCompaniesWorked'], dtype='object')


X1 = X1.drop(drpd_clms, axis = 1)
from sklearn.model_selection import train_test_split
xtrain, xtest,ytrain, ytest = train_test_split(X1, Y, test_size = 0.2, random_state=21)
from statsmodels.api import OLS, add_constant
xconst = add_constant(xtrain)
ols = OLS(ytrain, xconst)
model = ols.fit()
model.summary()


from re import M
q = pd.DataFrame(model.pvalues, columns = ["Pvalues"])
m = []
for i in q.Pvalues:
  m.append(round(i,3))

q.Pvalues = m
drpd_clms = q[q.Pvalues > 0.04].index
drpd_clms

Index(['const', 'Age', 'DailyRate', 'DistanceFromHome', 'Education',
       'JobInvolvement', 'JobLevel', 'MonthlyIncome', 'PerformanceRating',
       'StockOptionLevel', 'WorkLifeBalance', 'YearsAtCompany',
       'YearsSinceLastPromotion', 'YearsWithCurrManager',
       'BusinessTravel_Non-Travel', 'BusinessTravel_Travel_Frequently',
       'BusinessTravel_Travel_Rarely', 'Department_Human Resources',
       'Department_Research & Development', 'Department_Sales',
       'EducationField_Human Resources', 'EducationField_Life Sciences',
       'EducationField_Marketing', 'EducationField_Medical',
       'EducationField_Other', 'EducationField_Technical Degree',
       'Gender_Female', 'Gender_Male', 'JobRole_Healthcare Representative',
       'JobRole_Human Resources', 'JobRole_Laboratory Technician',
       'JobRole_Manager', 'JobRole_Manufacturing Director',
       'JobRole_Research Director', 'JobRole_Research Scientist',
       'JobRole_Sales Executive', 'JobRole_Sales Representative',
       'MaritalStatus_Divorced', 'MaritalStatus_Married',
       'MaritalStatus_Single', 'OverTime_No', 'OverTime_Yes'],
      dtype='object')


from sklearn.model_selection import train_test_split
xtrain, xtest,ytrain, ytest = train_test_split(X1, Y, test_size = 0.2, random_state=21)


from sklearn.linear_model import LinearRegression
lr = LinearRegression()
modelr = lr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 6019.876222363946
test Error ====> 6430.699723639455


cat = []
con = []
for i in A.columns:
    if A[i].dtypes =="object":
        cat.append(i)
    else:
        con.append(i)

con

['Attrition',
 'Age',
 'DailyRate',
 'DistanceFromHome',
 'Education',
 'EmployeeCount',
 'EmployeeNumber',
 'EnvironmentSatisfaction',
 'HourlyRate',
 'JobInvolvement',
 'JobLevel',
 'JobSatisfaction',
 'MonthlyIncome',
 'MonthlyRate',
 'NumCompaniesWorked',
 'PercentSalaryHike',
 'PerformanceRating',
 'RelationshipSatisfaction',
 'StandardHours',
 'StockOptionLevel',
 'TotalWorkingYears',
 'TrainingTimesLastYear',
 'WorkLifeBalance',
 'YearsAtCompany',
 'YearsInCurrentRole',
 'YearsSinceLastPromotion',
 'YearsWithCurrManager']

cat

['BusinessTravel',
 'Department',
 'EducationField',
 'Gender',
 'JobRole',
 'MaritalStatus',
 'Over18',
 'OverTime']


from statsmodels.api import OLS
from statsmodels.formula.api import ols
model = ols("EmployeeCount ~  MaritalStatus" , A).fit()
from statsmodels.stats.anova import anova_lm
anova_result = anova_lm(model)
Df = pd.DataFrame(anova_result)
Df


from statsmodels.api import OLS
from statsmodels.formula.api import ols
model = ols("EmployeeCount ~  MaritalStatus" , A).fit()
from statsmodels.stats.anova import anova_lm
anova_result = anova_lm(model)
Df = pd.DataFrame(anova_result)
q = Df['PR(>F)'][0]
q

0.05288312282841075


from statsmodels.api import OLS
from statsmodels.formula.api import ols
model = ols("EmployeeCount ~  MaritalStatus" , A).fit()
from statsmodels.stats.anova import anova_lm
anova_result = anova_lm(model)
Df = pd.DataFrame(anova_result)
q = Df['PR(>F)']["MaritalStatus"]
round(q,4)

0.0529


def anova(df, cat, con):
    from statsmodels.formula.api import ols
    from statsmodels.stats.anova import anova_lm
    rel = con + "~" + cat
    model = ols(rel,df).fit()
    anova_result = anova_lm(model)
    Df = pd.DataFrame(anova_result)
    q = Df['PR(>F)'][cat]
    q = round(q,4)
    return q


anova(A,'MaritalStatus' ,'EmployeeCount')

0.0529


d = pd.DataFrame([cat,con]).T
d.columns = ["Cate", "Cont"]
d


v= list(d["Cont"])
v[0]

'Attrition'


a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)
a = anova(A,'BusinessTravel','Age' )
print('BusinessTravel ~ Age:',a)
a = anova(A,'BusinessTravel','DailyRate' )
print('BusinessTravel ~ DailyRate:',a)
a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)
a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)
a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)
a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)
a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)
a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)
a = anova(A,'BusinessTravel','Attrition' )
print('BusinessTravel ~ Attrition:',a)

BusinessTravel ~ Attrition: 0.0
BusinessTravel ~ Age: 0.5357
BusinessTravel ~ DailyRate: 0.8354
BusinessTravel ~ Attrition: 0.0
BusinessTravel ~ Attrition: 0.0
BusinessTravel ~ Attrition: 0.0
BusinessTravel ~ Attrition: 0.0
BusinessTravel ~ Attrition: 0.0
BusinessTravel ~ Attrition: 0.0
BusinessTravel ~ Attrition: 0.0


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    for j in range(0,6):
        c = list(d["Cate"])
        a = anova(A,c[j],v[i] )
        print(c[j], '~', v[i],a)
    print("================================")

Anova Testing

BusinessTravel ~ Attrition 0.0
Department ~ Attrition 0.0045
EducationField ~ Attrition 0.0067
Gender ~ Attrition 0.2591
JobRole ~ Attrition 0.0
MaritalStatus ~ Attrition 0.0
================================
BusinessTravel ~ Age 0.5357
Department ~ Age 0.4653
EducationField ~ Age 0.3473
Gender ~ Age 0.1641
JobRole ~ Age 0.0
MaritalStatus ~ Age 0.0
================================
BusinessTravel ~ DailyRate 0.8354
Department ~ DailyRate 0.5686
EducationField ~ DailyRate 0.0594
Gender ~ DailyRate 0.6535
JobRole ~ DailyRate 0.9238
MaritalStatus ~ DailyRate 0.0135
================================
BusinessTravel ~ DistanceFromHome 0.6258
Department ~ DistanceFromHome 0.7906
EducationField ~ DistanceFromHome 0.6829
Gender ~ DistanceFromHome 0.9435
JobRole ~ DistanceFromHome 0.6181
MaritalStatus ~ DistanceFromHome 0.4693
================================
BusinessTravel ~ Education 0.9436
Department ~ Education 0.7535
EducationField ~ Education 0.006
Gender ~ Education 0.5261
JobRole ~ Education 0.0014
MaritalStatus ~ Education 0.9866
================================
BusinessTravel ~ EmployeeCount 0.0869
Department ~ EmployeeCount 0.0
EducationField ~ EmployeeCount 0.0
Gender ~ EmployeeCount 0.0
JobRole ~ EmployeeCount 0.0
MaritalStatus ~ EmployeeCount 0.0529
================================
BusinessTravel ~ EmployeeNumber 0.685
Department ~ EmployeeNumber 0.0333
EducationField ~ EmployeeNumber 0.7993
Gender ~ EmployeeNumber 0.3875
JobRole ~ EmployeeNumber 0.1985
MaritalStatus ~ EmployeeNumber 0.1178
================================
BusinessTravel ~ EnvironmentSatisfaction 0.8881
Department ~ EnvironmentSatisfaction 0.563
EducationField ~ EnvironmentSatisfaction 0.164
Gender ~ EnvironmentSatisfaction 0.9845
JobRole ~ EnvironmentSatisfaction 0.2838
MaritalStatus ~ EnvironmentSatisfaction 0.6766
================================
BusinessTravel ~ HourlyRate 0.5696
Department ~ HourlyRate 0.701
EducationField ~ HourlyRate 0.3052
Gender ~ HourlyRate 0.9854
JobRole ~ HourlyRate 0.8967
MaritalStatus ~ HourlyRate 0.3302
================================
BusinessTravel ~ JobInvolvement 0.2132
Department ~ JobInvolvement 0.6059
EducationField ~ JobInvolvement 0.9636
Gender ~ JobInvolvement 0.4914
JobRole ~ JobInvolvement 0.6629
MaritalStatus ~ JobInvolvement 0.2223
================================
BusinessTravel ~ JobLevel 0.6513
Department ~ JobLevel 0.0001
EducationField ~ JobLevel 0.0065
Gender ~ JobLevel 0.131
JobRole ~ JobLevel 0.0
MaritalStatus ~ JobLevel 0.0037
================================
BusinessTravel ~ JobSatisfaction 0.3748
Department ~ JobSatisfaction 0.6053
EducationField ~ JobSatisfaction 0.4438
Gender ~ JobSatisfaction 0.2026
JobRole ~ JobSatisfaction 0.939
MaritalStatus ~ JobSatisfaction 0.6216
================================
BusinessTravel ~ MonthlyIncome 0.3282
Department ~ MonthlyIncome 0.041
EducationField ~ MonthlyIncome 0.08
Gender ~ MonthlyIncome 0.2222
JobRole ~ MonthlyIncome 0.0
MaritalStatus ~ MonthlyIncome 0.0028
================================
BusinessTravel ~ MonthlyRate 0.8376
Department ~ MonthlyRate 0.5697
EducationField ~ MonthlyRate 0.7529
Gender ~ MonthlyRate 0.1119
JobRole ~ MonthlyRate 0.779
MaritalStatus ~ MonthlyRate 0.3097
================================
BusinessTravel ~ NumCompaniesWorked 0.3108
Department ~ NumCompaniesWorked 0.3863
EducationField ~ NumCompaniesWorked 0.7041
Gender ~ NumCompaniesWorked 0.1336
JobRole ~ NumCompaniesWorked 0.0
MaritalStatus ~ NumCompaniesWorked 0.2899
================================
BusinessTravel ~ PercentSalaryHike 0.3741
Department ~ PercentSalaryHike 0.397
EducationField ~ PercentSalaryHike 0.3831
Gender ~ PercentSalaryHike 0.9166
JobRole ~ PercentSalaryHike 0.3578
MaritalStatus ~ PercentSalaryHike 0.6132
================================
BusinessTravel ~ PerformanceRating 0.5882
Department ~ PerformanceRating 0.4522
EducationField ~ PerformanceRating 0.8374
Gender ~ PerformanceRating 0.5955
JobRole ~ PerformanceRating 0.5268
MaritalStatus ~ PerformanceRating 0.9069
================================
BusinessTravel ~ RelationshipSatisfaction 0.334
Department ~ RelationshipSatisfaction 0.4059
EducationField ~ RelationshipSatisfaction 0.4794
Gender ~ RelationshipSatisfaction 0.3809
JobRole ~ RelationshipSatisfaction 0.7806
MaritalStatus ~ RelationshipSatisfaction 0.2012
================================
BusinessTravel ~ StandardHours 0.8197
Department ~ StandardHours 0.0
EducationField ~ StandardHours 0.0
Gender ~ StandardHours 0.0006
JobRole ~ StandardHours 0.0
MaritalStatus ~ StandardHours 0.0
================================
BusinessTravel ~ StockOptionLevel 0.4926
Department ~ StockOptionLevel 0.8099
EducationField ~ StockOptionLevel 0.3059
Gender ~ StockOptionLevel 0.6262
JobRole ~ StockOptionLevel 0.7559
MaritalStatus ~ StockOptionLevel 0.0
================================
BusinessTravel ~ TotalWorkingYears 0.4201
Department ~ TotalWorkingYears 0.8332
EducationField ~ TotalWorkingYears 0.4137
Gender ~ TotalWorkingYears 0.0724
JobRole ~ TotalWorkingYears 0.0
MaritalStatus ~ TotalWorkingYears 0.0027
================================
BusinessTravel ~ TrainingTimesLastYear 0.7249
Department ~ TrainingTimesLastYear 0.2348
EducationField ~ TrainingTimesLastYear 0.0854
Gender ~ TrainingTimesLastYear 0.1372
JobRole ~ TrainingTimesLastYear 0.1736
MaritalStatus ~ TrainingTimesLastYear 0.5068
================================
BusinessTravel ~ WorkLifeBalance 0.8887
Department ~ WorkLifeBalance 0.015
EducationField ~ WorkLifeBalance 0.5667
Gender ~ WorkLifeBalance 0.916
JobRole ~ WorkLifeBalance 0.0729
MaritalStatus ~ WorkLifeBalance 0.84
================================
BusinessTravel ~ YearsAtCompany 0.8227
Department ~ YearsAtCompany 0.4669
EducationField ~ YearsAtCompany 0.6489
Gender ~ YearsAtCompany 0.2544
JobRole ~ YearsAtCompany 0.0
MaritalStatus ~ YearsAtCompany 0.0247
================================
BusinessTravel ~ YearsInCurrentRole 0.8982
Department ~ YearsInCurrentRole 0.0848
EducationField ~ YearsInCurrentRole 0.6575
Gender ~ YearsInCurrentRole 0.1119
JobRole ~ YearsInCurrentRole 0.0
MaritalStatus ~ YearsInCurrentRole 0.0035
================================
BusinessTravel ~ YearsSinceLastPromotion 0.4269
Department ~ YearsSinceLastPromotion 0.2946
EducationField ~ YearsSinceLastPromotion 0.6069
Gender ~ YearsSinceLastPromotion 0.3012
JobRole ~ YearsSinceLastPromotion 0.0
MaritalStatus ~ YearsSinceLastPromotion 0.075
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'BusinessTravel',v[i] )
    print('BusinessTravel', '~', v[i],a)
    print("================================")

Anova Testing

BusinessTravel ~ Attrition 0.0
================================
BusinessTravel ~ Age 0.5357
================================
BusinessTravel ~ DailyRate 0.8354
================================
BusinessTravel ~ DistanceFromHome 0.6258
================================
BusinessTravel ~ Education 0.9436
================================
BusinessTravel ~ EmployeeCount 0.0869
================================
BusinessTravel ~ EmployeeNumber 0.685
================================
BusinessTravel ~ EnvironmentSatisfaction 0.8881
================================
BusinessTravel ~ HourlyRate 0.5696
================================
BusinessTravel ~ JobInvolvement 0.2132
================================
BusinessTravel ~ JobLevel 0.6513
================================
BusinessTravel ~ JobSatisfaction 0.3748
================================
BusinessTravel ~ MonthlyIncome 0.3282
================================
BusinessTravel ~ MonthlyRate 0.8376
================================
BusinessTravel ~ NumCompaniesWorked 0.3108
================================
BusinessTravel ~ PercentSalaryHike 0.3741
================================
BusinessTravel ~ PerformanceRating 0.5882
================================
BusinessTravel ~ RelationshipSatisfaction 0.334
================================
BusinessTravel ~ StandardHours 0.8197
================================
BusinessTravel ~ StockOptionLevel 0.4926
================================
BusinessTravel ~ TotalWorkingYears 0.4201
================================
BusinessTravel ~ TrainingTimesLastYear 0.7249
================================
BusinessTravel ~ WorkLifeBalance 0.8887
================================
BusinessTravel ~ YearsAtCompany 0.8227
================================
BusinessTravel ~ YearsInCurrentRole 0.8982
================================
BusinessTravel ~ YearsSinceLastPromotion 0.4269
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'Department',v[i] )
    print('Department', '~', v[i],a)
    print("================================")

Anova Testing

Department ~ Attrition 0.0045
================================
Department ~ Age 0.4653
================================
Department ~ DailyRate 0.5686
================================
Department ~ DistanceFromHome 0.7906
================================
Department ~ Education 0.7535
================================
Department ~ EmployeeCount 0.0
================================
Department ~ EmployeeNumber 0.0333
================================
Department ~ EnvironmentSatisfaction 0.563
================================
Department ~ HourlyRate 0.701
================================
Department ~ JobInvolvement 0.6059
================================
Department ~ JobLevel 0.0001
================================
Department ~ JobSatisfaction 0.6053
================================
Department ~ MonthlyIncome 0.041
================================
Department ~ MonthlyRate 0.5697
================================
Department ~ NumCompaniesWorked 0.3863
================================
Department ~ PercentSalaryHike 0.397
================================
Department ~ PerformanceRating 0.4522
================================
Department ~ RelationshipSatisfaction 0.4059
================================
Department ~ StandardHours 0.0
================================
Department ~ StockOptionLevel 0.8099
================================
Department ~ TotalWorkingYears 0.8332
================================
Department ~ TrainingTimesLastYear 0.2348
================================
Department ~ WorkLifeBalance 0.015
================================
Department ~ YearsAtCompany 0.4669
================================
Department ~ YearsInCurrentRole 0.0848
================================
Department ~ YearsSinceLastPromotion 0.2946
================================


list(d['Cate'])

['BusinessTravel',
 'Department',
 'EducationField',
 'Gender',
 'JobRole',
 'MaritalStatus',
 'Over18',
 'OverTime',
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None,
 None]


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'Department',v[i] )
    print('Department', '~', v[i],a)
    print("================================")

Anova Testing

Department ~ Attrition 0.0045
================================
Department ~ Age 0.4653
================================
Department ~ DailyRate 0.5686
================================
Department ~ DistanceFromHome 0.7906
================================
Department ~ Education 0.7535
================================
Department ~ EmployeeCount 0.0
================================
Department ~ EmployeeNumber 0.0333
================================
Department ~ EnvironmentSatisfaction 0.563
================================
Department ~ HourlyRate 0.701
================================
Department ~ JobInvolvement 0.6059
================================
Department ~ JobLevel 0.0001
================================
Department ~ JobSatisfaction 0.6053
================================
Department ~ MonthlyIncome 0.041
================================
Department ~ MonthlyRate 0.5697
================================
Department ~ NumCompaniesWorked 0.3863
================================
Department ~ PercentSalaryHike 0.397
================================
Department ~ PerformanceRating 0.4522
================================
Department ~ RelationshipSatisfaction 0.4059
================================
Department ~ StandardHours 0.0
================================
Department ~ StockOptionLevel 0.8099
================================
Department ~ TotalWorkingYears 0.8332
================================
Department ~ TrainingTimesLastYear 0.2348
================================
Department ~ WorkLifeBalance 0.015
================================
Department ~ YearsAtCompany 0.4669
================================
Department ~ YearsInCurrentRole 0.0848
================================
Department ~ YearsSinceLastPromotion 0.2946
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'EducationField',v[i] )
    print('EducationField', '~', v[i],a)
    print("================================")

Anova Testing

EducationField ~ Attrition 0.0067
================================
EducationField ~ Age 0.3473
================================
EducationField ~ DailyRate 0.0594
================================
EducationField ~ DistanceFromHome 0.6829
================================
EducationField ~ Education 0.006
================================
EducationField ~ EmployeeCount 0.0
================================
EducationField ~ EmployeeNumber 0.7993
================================
EducationField ~ EnvironmentSatisfaction 0.164
================================
EducationField ~ HourlyRate 0.3052
================================
EducationField ~ JobInvolvement 0.9636
================================
EducationField ~ JobLevel 0.0065
================================
EducationField ~ JobSatisfaction 0.4438
================================
EducationField ~ MonthlyIncome 0.08
================================
EducationField ~ MonthlyRate 0.7529
================================
EducationField ~ NumCompaniesWorked 0.7041
================================
EducationField ~ PercentSalaryHike 0.3831
================================
EducationField ~ PerformanceRating 0.8374
================================
EducationField ~ RelationshipSatisfaction 0.4794
================================
EducationField ~ StandardHours 0.0
================================
EducationField ~ StockOptionLevel 0.3059
================================
EducationField ~ TotalWorkingYears 0.4137
================================
EducationField ~ TrainingTimesLastYear 0.0854
================================
EducationField ~ WorkLifeBalance 0.5667
================================
EducationField ~ YearsAtCompany 0.6489
================================
EducationField ~ YearsInCurrentRole 0.6575
================================
EducationField ~ YearsSinceLastPromotion 0.6069
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'Gender',v[i] )
    print('Gender', '~', v[i],a)
    print("================================")

Anova Testing

Gender ~ Attrition 0.2591
================================
Gender ~ Age 0.1641
================================
Gender ~ DailyRate 0.6535
================================
Gender ~ DistanceFromHome 0.9435
================================
Gender ~ Education 0.5261
================================
Gender ~ EmployeeCount 0.0
================================
Gender ~ EmployeeNumber 0.3875
================================
Gender ~ EnvironmentSatisfaction 0.9845
================================
Gender ~ HourlyRate 0.9854
================================
Gender ~ JobInvolvement 0.4914
================================
Gender ~ JobLevel 0.131
================================
Gender ~ JobSatisfaction 0.2026
================================
Gender ~ MonthlyIncome 0.2222
================================
Gender ~ MonthlyRate 0.1119
================================
Gender ~ NumCompaniesWorked 0.1336
================================
Gender ~ PercentSalaryHike 0.9166
================================
Gender ~ PerformanceRating 0.5955
================================
Gender ~ RelationshipSatisfaction 0.3809
================================
Gender ~ StandardHours 0.0006
================================
Gender ~ StockOptionLevel 0.6262
================================
Gender ~ TotalWorkingYears 0.0724
================================
Gender ~ TrainingTimesLastYear 0.1372
================================
Gender ~ WorkLifeBalance 0.916
================================
Gender ~ YearsAtCompany 0.2544
================================
Gender ~ YearsInCurrentRole 0.1119
================================
Gender ~ YearsSinceLastPromotion 0.3012
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'JobRole',v[i] )
    print('JobRole', '~', v[i],a)
    print("================================")

Anova Testing

JobRole ~ Attrition 0.0
================================
JobRole ~ Age 0.0
================================
JobRole ~ DailyRate 0.9238
================================
JobRole ~ DistanceFromHome 0.6181
================================
JobRole ~ Education 0.0014
================================
JobRole ~ EmployeeCount 0.0
================================
JobRole ~ EmployeeNumber 0.1985
================================
JobRole ~ EnvironmentSatisfaction 0.2838
================================
JobRole ~ HourlyRate 0.8967
================================
JobRole ~ JobInvolvement 0.6629
================================
JobRole ~ JobLevel 0.0
================================
JobRole ~ JobSatisfaction 0.939
================================
JobRole ~ MonthlyIncome 0.0
================================
JobRole ~ MonthlyRate 0.779
================================
JobRole ~ NumCompaniesWorked 0.0
================================
JobRole ~ PercentSalaryHike 0.3578
================================
JobRole ~ PerformanceRating 0.5268
================================
JobRole ~ RelationshipSatisfaction 0.7806
================================
JobRole ~ StandardHours 0.0
================================
JobRole ~ StockOptionLevel 0.7559
================================
JobRole ~ TotalWorkingYears 0.0
================================
JobRole ~ TrainingTimesLastYear 0.1736
================================
JobRole ~ WorkLifeBalance 0.0729
================================
JobRole ~ YearsAtCompany 0.0
================================
JobRole ~ YearsInCurrentRole 0.0
================================
JobRole ~ YearsSinceLastPromotion 0.0
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'MaritalStatus',v[i] )
    print('MaritalStatus', '~', v[i],a)
    print("================================")

Anova Testing

MaritalStatus ~ Attrition 0.0
================================
MaritalStatus ~ Age 0.0
================================
MaritalStatus ~ DailyRate 0.0135
================================
MaritalStatus ~ DistanceFromHome 0.4693
================================
MaritalStatus ~ Education 0.9866
================================
MaritalStatus ~ EmployeeCount 0.0529
================================
MaritalStatus ~ EmployeeNumber 0.1178
================================
MaritalStatus ~ EnvironmentSatisfaction 0.6766
================================
MaritalStatus ~ HourlyRate 0.3302
================================
MaritalStatus ~ JobInvolvement 0.2223
================================
MaritalStatus ~ JobLevel 0.0037
================================
MaritalStatus ~ JobSatisfaction 0.6216
================================
MaritalStatus ~ MonthlyIncome 0.0028
================================
MaritalStatus ~ MonthlyRate 0.3097
================================
MaritalStatus ~ NumCompaniesWorked 0.2899
================================
MaritalStatus ~ PercentSalaryHike 0.6132
================================
MaritalStatus ~ PerformanceRating 0.9069
================================
MaritalStatus ~ RelationshipSatisfaction 0.2012
================================
MaritalStatus ~ StandardHours 0.0
================================
MaritalStatus ~ StockOptionLevel 0.0
================================
MaritalStatus ~ TotalWorkingYears 0.0027
================================
MaritalStatus ~ TrainingTimesLastYear 0.5068
================================
MaritalStatus ~ WorkLifeBalance 0.84
================================
MaritalStatus ~ YearsAtCompany 0.0247
================================
MaritalStatus ~ YearsInCurrentRole 0.0035
================================
MaritalStatus ~ YearsSinceLastPromotion 0.075
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'Over18',v[i] )
    print('Over18', '~', v[i],a)
    print("================================")

Anova Testing

Over18 ~ Attrition nan
================================
Over18 ~ Age nan
================================
Over18 ~ DailyRate nan
================================
Over18 ~ DistanceFromHome nan
================================
Over18 ~ Education nan
================================
Over18 ~ EmployeeCount nan
================================
Over18 ~ EmployeeNumber nan
================================
Over18 ~ EnvironmentSatisfaction nan
================================
Over18 ~ HourlyRate nan
================================
Over18 ~ JobInvolvement nan
================================
Over18 ~ JobLevel nan
================================
Over18 ~ JobSatisfaction nan
================================
Over18 ~ MonthlyIncome nan
================================
Over18 ~ MonthlyRate nan
================================
Over18 ~ NumCompaniesWorked nan
================================
Over18 ~ PercentSalaryHike nan
================================
Over18 ~ PerformanceRating nan
================================
Over18 ~ RelationshipSatisfaction nan
================================
Over18 ~ StandardHours nan
================================
Over18 ~ StockOptionLevel nan
================================
Over18 ~ TotalWorkingYears nan
================================
Over18 ~ TrainingTimesLastYear nan
================================
Over18 ~ WorkLifeBalance nan
================================
Over18 ~ YearsAtCompany nan
================================
Over18 ~ YearsInCurrentRole nan
================================
Over18 ~ YearsSinceLastPromotion nan
================================


print("Anova Testing\n")
for i in range(0,26):
    v= list(d["Cont"])
    
    a = anova(A,'OverTime',v[i] )
    print('OverTime', '~', v[i],a)
    print("================================")

Anova Testing

OverTime ~ Attrition 0.0
================================
OverTime ~ Age 0.2823
================================
OverTime ~ DailyRate 0.7264
================================
OverTime ~ DistanceFromHome 0.3283
================================
OverTime ~ Education 0.4362
================================
OverTime ~ EmployeeCount 0.7364
================================
OverTime ~ EmployeeNumber 0.3571
================================
OverTime ~ EnvironmentSatisfaction 0.0071
================================
OverTime ~ HourlyRate 0.7656
================================
OverTime ~ JobInvolvement 0.8931
================================
OverTime ~ JobLevel 0.9834
================================
OverTime ~ JobSatisfaction 0.3471
================================
OverTime ~ MonthlyIncome 0.8156
================================
OverTime ~ MonthlyRate 0.4116
================================
OverTime ~ NumCompaniesWorked 0.4258
================================
OverTime ~ PercentSalaryHike 0.8351
================================
OverTime ~ PerformanceRating 0.8671
================================
OverTime ~ RelationshipSatisfaction 0.0631
================================
OverTime ~ StandardHours 0.0
================================
OverTime ~ StockOptionLevel 0.9863
================================
OverTime ~ TotalWorkingYears 0.6251
================================
OverTime ~ TrainingTimesLastYear 0.0024
================================
OverTime ~ WorkLifeBalance 0.2993
================================
OverTime ~ YearsAtCompany 0.6544
================================
OverTime ~ YearsInCurrentRole 0.2542
================================
OverTime ~ YearsSinceLastPromotion 0.6392
================================


from sklearn.model_selection import train_test_split
xtrain,xtest,ytrain,ytest = train_test_split(X,Y,test_size=0.2, random_state=40)


from sklearn.linear_model import LinearRegression
lr = LinearRegression()
modelr = lr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 6085.18112244898
test Error ====> 6192.034013605442


tuning_grid = []
x = 0.1
for i in range(0,100):
    x = x + 0.001
    x = round(x,3)
    tuning_grid.append(x)
    
tp = {"alpha":tuning_grid}


from sklearn.linear_model import LinearRegression, Lasso, Ridge
l1 = Lasso()
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(l1,tp, scoring="neg_mean_absolute_error", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'alpha': 0.2}


bestval['alpha']

0.2


from sklearn.linear_model import LinearRegression
l1 = Lasso(alpha=1.1)
modelr = l1.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 6086.951500456323
test Error ====> 6190.808984256642


tuning_grid = []
x = 0.1
for i in range(0,100):
    x = x + 0.001
    x = round(x,3)
    tuning_grid.append(x)
    
tp = {"alpha":tuning_grid}


from sklearn.linear_model import LinearRegression, Lasso, Ridge
l2 = Ridge()
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(l2,tp, scoring="neg_mean_absolute_error", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'alpha': 0.2}


bestval['alpha']

0.2


from sklearn.linear_model import LinearRegression
l2 = Ridge(alpha=0.2)
modelr = l2.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 6085.490591922476
test Error ====> 6192.036507274708


from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor()
tp = {"max_depth":range(10), "random_state": range(40)}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(dtr,tp, scoring="neg_mean_absolute_error", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'max_depth': 1, 'random_state': 0}


print("bestval['max_depth']: ",bestval['max_depth'])
print("bestval['random_state']: ",bestval['random_state'])

bestval['max_depth']:  1
bestval['random_state']:  0


from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor(random_state=bestval['random_state'], max_depth=bestval['max_depth'])
modelr = dtr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 6183.431233742428
test Error ====> 6089.34256559767


from sklearn.ensemble import RandomForestRegressor 
rfr = RandomForestRegressor()
tp = {"n_estimators": range(30)}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(rfr,tp, scoring="neg_mean_absolute_error", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'n_estimators': 21}


rfr = RandomForestRegressor(random_state=1,n_estimators=bestval['n_estimators'])
modelr = rfr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 2483.517614188532
test Error ====> 6113.909297052155


from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
dtr = DecisionTreeRegressor(random_state=20,max_depth=2)
abr = AdaBoostRegressor(dtr)
tp = {"n_estimators": range(40)}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(abr,tp, scoring="neg_mean_absolute_error", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'n_estimators': 19}


from sklearn.tree import DecisionTreeRegressor
dtr = DecisionTreeRegressor(random_state= 21, max_depth=2)
abr = AdaBoostRegressor(random_state=20, n_estimators=bestval['n_estimators'])
modelr = abr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 6110.792632381813
test Error ====> 6004.663636873536


from sklearn.neighbors import KNeighborsRegressor
knr = KNeighborsRegressor()
tp = {"n_neighbors": range(40)}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(knr,tp, scoring="neg_mean_absolute_error", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'n_neighbors': 32}


from sklearn.neighbors import KNeighborsRegressor
knr = KNeighborsRegressor(n_neighbors=bestval['n_neighbors'])
modelr = knr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 6069.264482355442
test Error ====> 6080.711096938776


from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
dtr = DecisionTreeRegressor(random_state=20,max_depth=2)
abr =XGBRegressor()
tp = {"n_estimators": range(15),"booster": ['gbtree', 'gblinear','dart']}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(abr,tp, scoring="neg_mean_absolute_error", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

[13:27:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
[13:27:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.

{'booster': 'gblinear', 'n_estimators': 14}


xgr = XGBRegressor(n_estimators=8, max_depth=1, booster = "gblinear")
modelr = xgr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

[13:27:26] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
train Error ====> 6424.304176797672
test Error ====> 6432.736377949617


from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
modelr = gnb.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import mean_absolute_error
print("train Error ====>", round(mean_absolute_error(predtr, ytrain),20))
print("test Error ====>", round(mean_absolute_error(predts, ytest),20))

train Error ====> 0.0
test Error ====> 7579.108843537415


from keras.layers import Dense, Dropout
from keras.models import Sequential 
!pip install keras_tuner
import keras_tuner as kt

Collecting keras_tuner
  Downloading keras_tuner-1.1.0-py3-none-any.whl (98 kB)
     |████████████████████████████████| 98 kB 3.7 MB/s 
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (1.4.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (1.19.5)
Collecting kt-legacy
  Downloading kt_legacy-1.0.4-py3-none-any.whl (9.6 kB)
Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (2.7.0)
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (2.23.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (21.3)
Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (5.5.0)
Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (2.6.1)
Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (0.8.1)
Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (4.8.0)
Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (1.0.18)
Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (0.7.5)
Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (57.4.0)
Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (4.4.2)
Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (5.1.1)
Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->keras_tuner) (1.15.0)
Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->keras_tuner) (0.2.5)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->keras_tuner) (3.0.7)
Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython->keras_tuner) (0.7.0)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (2021.10.8)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (1.24.3)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (2.10)
Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.35.0)
Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.0.0)
Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (0.6.1)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (3.3.6)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (0.4.6)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.0.1)
Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (3.17.3)
Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.43.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (0.37.1)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.8.1)
Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras_tuner) (4.8)
Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras_tuner) (4.2.4)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras_tuner) (0.2.8)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras_tuner) (1.3.0)
Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->keras_tuner) (4.10.1)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras_tuner) (3.7.0)
Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras_tuner) (3.10.0.2)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->keras_tuner) (0.4.8)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras_tuner) (3.1.1)
Installing collected packages: kt-legacy, keras-tuner
Successfully installed keras-tuner-1.1.0 kt-legacy-1.0.4


def model_builder(hp):
    nn = Sequential()
    nn.add(Dense(128,input_dim=xtrain.shape[1]))
    x = hp.Int("units",min_value=32,max_value=400,step=32)
    nn.add(Dense(units=x))
    y = hp.Choice("rate",values=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9])
    nn.add(Dropout(rate=y))
    nn.add(Dense(1,activation="relu"))
    nn.compile(loss="mean_absolute_error")
    return nn


import keras_tuner as kt
tuner = kt.Hyperband(model_builder,
            objective="val_loss",
            max_epochs=10,
            directory="/content/drive/MyDrive/python/Attrition_Project/AtronN4",
            project_name="kk2")


tuner.search(xtrain,ytrain,epochs=500, validation_split=0.2)

Trial 30 Complete [00h 00m 04s]
val_loss: 11971.455078125

Best val_loss So Far: 6357.6396484375
Total elapsed time: 00h 01m 34s
INFO:tensorflow:Oracle triggered exit


tuner.get_best_hyperparameters()[0].get("units")

384


tuner.get_best_hyperparameters()[0].get("rate")

0.4


import keras_tuner as kt
tuner = kt.Hyperband(model_builder,
            objective="loss",
            max_epochs=10,
            directory="/content/drive/MyDrive/python/Attrition_Project/AtronN4",
            project_name="k21")


tuner.search(xtrain,ytrain,epochs=500, validation_split=0.2)

Trial 30 Complete [00h 00m 04s]
loss: 7609.0009765625

Best loss So Far: 7609.0009765625
Total elapsed time: 00h 01m 25s
INFO:tensorflow:Oracle triggered exit


tuner.get_best_hyperparameters()[0].get("units")

192


tuner.get_best_hyperparameters()[0].get("rate")

0.2


from scipy.stats.stats import moment
from keras.callbacks import EarlyStopping

es = EarlyStopping(patience=25,monitor="val_loss")

nn = Sequential()
nn.add(Dense(1260,input_dim=X.shape[1]))
nn.add(Dense(1260, activation="relu"))
nn.add(Dropout(0.3))
nn.add(Dense(1260, activation="relu"))
nn.add(Dropout(0.3))
nn.add(Dense(1260, activation="relu"))
nn.add(Dropout(0.3))
nn.add(Dense(1260, activation="relu"))
nn.add(Dropout(0.3))

nn.add(Dense(1,activation="relu"))
nn.compile( loss="mean_absolute_error")

model = nn.fit(X,Y,epochs = 500, validation_split = 0.2, callbacks=es)

Epoch 1/500
37/37 [==============================] - 3s 35ms/step - loss: 8086.0264 - val_loss: 7812.4517
Epoch 2/500
37/37 [==============================] - 1s 18ms/step - loss: 7022.2896 - val_loss: 7714.8027
Epoch 3/500
37/37 [==============================] - 1s 19ms/step - loss: 6559.5293 - val_loss: 6459.8794
Epoch 4/500
37/37 [==============================] - 1s 18ms/step - loss: 6582.2070 - val_loss: 6591.5391
Epoch 5/500
37/37 [==============================] - 1s 19ms/step - loss: 6514.3140 - val_loss: 7137.4131
Epoch 6/500
37/37 [==============================] - 1s 18ms/step - loss: 6290.7227 - val_loss: 6794.2456
Epoch 7/500
37/37 [==============================] - 1s 18ms/step - loss: 6156.3276 - val_loss: 6642.6646
Epoch 8/500
37/37 [==============================] - 1s 20ms/step - loss: 6186.4062 - val_loss: 7029.2778
Epoch 9/500
37/37 [==============================] - 1s 20ms/step - loss: 5937.5264 - val_loss: 7053.5972
Epoch 10/500
37/37 [==============================] - 1s 18ms/step - loss: 5918.4893 - val_loss: 6648.6367
Epoch 11/500
37/37 [==============================] - 1s 21ms/step - loss: 5870.3735 - val_loss: 7624.2549
Epoch 12/500
37/37 [==============================] - 1s 18ms/step - loss: 5743.8989 - val_loss: 7232.0435
Epoch 13/500
37/37 [==============================] - 1s 18ms/step - loss: 5569.2202 - val_loss: 6807.2236
Epoch 14/500
37/37 [==============================] - 1s 20ms/step - loss: 5567.8257 - val_loss: 6808.4370
Epoch 15/500
37/37 [==============================] - 1s 18ms/step - loss: 5658.6294 - val_loss: 6660.9316
Epoch 16/500
37/37 [==============================] - 1s 18ms/step - loss: 5411.7397 - val_loss: 6752.2969
Epoch 17/500
37/37 [==============================] - 1s 18ms/step - loss: 5327.5342 - val_loss: 6759.5679
Epoch 18/500
37/37 [==============================] - 1s 19ms/step - loss: 5291.0640 - val_loss: 6816.9600
Epoch 19/500
37/37 [==============================] - 1s 18ms/step - loss: 5387.5918 - val_loss: 6886.0308
Epoch 20/500
37/37 [==============================] - 1s 19ms/step - loss: 5336.6724 - val_loss: 6891.5610
Epoch 21/500
37/37 [==============================] - 1s 18ms/step - loss: 5260.6641 - val_loss: 7405.2397
Epoch 22/500
37/37 [==============================] - 1s 15ms/step - loss: 5166.7495 - val_loss: 6799.7026
Epoch 23/500
37/37 [==============================] - 0s 13ms/step - loss: 5166.4790 - val_loss: 6715.3169
Epoch 24/500
37/37 [==============================] - 0s 13ms/step - loss: 5128.6567 - val_loss: 6686.6650
Epoch 25/500
37/37 [==============================] - 0s 12ms/step - loss: 4951.1841 - val_loss: 6770.9043
Epoch 26/500
37/37 [==============================] - 0s 12ms/step - loss: 4978.5850 - val_loss: 6687.9688
Epoch 27/500
37/37 [==============================] - 0s 12ms/step - loss: 4892.4902 - val_loss: 6739.9717
Epoch 28/500
37/37 [==============================] - 0s 12ms/step - loss: 4864.3486 - val_loss: 7154.1558
Epoch 29/500
37/37 [==============================] - 0s 13ms/step - loss: 4619.3696 - val_loss: 6942.9287
Epoch 30/500
37/37 [==============================] - 0s 13ms/step - loss: 4647.4653 - val_loss: 6754.8286
Epoch 31/500
37/37 [==============================] - 0s 13ms/step - loss: 4672.5371 - val_loss: 6776.6318
Epoch 32/500
37/37 [==============================] - 0s 13ms/step - loss: 4496.3867 - val_loss: 7292.8076
Epoch 33/500
37/37 [==============================] - 0s 12ms/step - loss: 4469.7617 - val_loss: 7306.5322
Epoch 34/500
37/37 [==============================] - 0s 13ms/step - loss: 4400.0957 - val_loss: 6905.6958
Epoch 35/500
37/37 [==============================] - 0s 12ms/step - loss: 4194.0449 - val_loss: 6931.6733
Epoch 36/500
37/37 [==============================] - 0s 12ms/step - loss: 4205.0215 - val_loss: 7405.6860
Epoch 37/500
37/37 [==============================] - 0s 12ms/step - loss: 4090.6272 - val_loss: 6859.5151
Epoch 38/500
37/37 [==============================] - 0s 13ms/step - loss: 4075.2764 - val_loss: 6970.0391
Epoch 39/500
37/37 [==============================] - 0s 12ms/step - loss: 3943.4622 - val_loss: 7005.3379
Epoch 40/500
37/37 [==============================] - 0s 13ms/step - loss: 3803.1323 - val_loss: 6960.3843
Epoch 41/500
37/37 [==============================] - 0s 13ms/step - loss: 3804.1748 - val_loss: 6988.8374
Epoch 42/500
37/37 [==============================] - 0s 12ms/step - loss: 3846.7305 - val_loss: 7080.8599
Epoch 43/500
37/37 [==============================] - 0s 13ms/step - loss: 3837.3955 - val_loss: 7032.1626
Epoch 44/500
37/37 [==============================] - 0s 13ms/step - loss: 3683.0208 - val_loss: 7020.0674
Epoch 45/500
37/37 [==============================] - 0s 13ms/step - loss: 3653.8694 - val_loss: 7093.9829
Epoch 46/500
37/37 [==============================] - 0s 12ms/step - loss: 3527.7803 - val_loss: 7199.4771
Epoch 47/500
37/37 [==============================] - 0s 12ms/step - loss: 3592.4622 - val_loss: 7062.9399
Epoch 48/500
37/37 [==============================] - 0s 12ms/step - loss: 3481.5571 - val_loss: 7167.7397
Epoch 49/500
37/37 [==============================] - 0s 13ms/step - loss: 3458.5137 - val_loss: 7108.3721
Epoch 50/500
37/37 [==============================] - 0s 13ms/step - loss: 3314.6304 - val_loss: 7185.1743
Epoch 51/500
37/37 [==============================] - 0s 12ms/step - loss: 3260.8364 - val_loss: 7387.5010
Epoch 52/500
37/37 [==============================] - 0s 12ms/step - loss: 3329.1328 - val_loss: 7164.5664
Epoch 53/500
37/37 [==============================] - 0s 13ms/step - loss: 3216.0166 - val_loss: 7023.8828
Epoch 54/500
37/37 [==============================] - 0s 12ms/step - loss: 3455.6284 - val_loss: 6990.6504
Epoch 55/500
37/37 [==============================] - 0s 12ms/step - loss: 3325.9111 - val_loss: 7306.5527
Epoch 56/500
37/37 [==============================] - 0s 12ms/step - loss: 3181.2168 - val_loss: 7099.6377
Epoch 57/500
37/37 [==============================] - 0s 12ms/step - loss: 3226.9126 - val_loss: 6842.5151
Epoch 58/500
37/37 [==============================] - 0s 12ms/step - loss: 3161.5691 - val_loss: 7058.3765
Epoch 59/500
37/37 [==============================] - 0s 13ms/step - loss: 3091.9646 - val_loss: 7074.0635
Epoch 60/500
37/37 [==============================] - 0s 13ms/step - loss: 3129.6216 - val_loss: 7396.5698
Epoch 61/500
37/37 [==============================] - 0s 12ms/step - loss: 3005.3240 - val_loss: 6868.6924
Epoch 62/500
37/37 [==============================] - 0s 12ms/step - loss: 3041.7668 - val_loss: 7732.1733
Epoch 63/500
37/37 [==============================] - 0s 12ms/step - loss: 3105.6257 - val_loss: 7102.4404
Epoch 64/500
37/37 [==============================] - 0s 13ms/step - loss: 2922.6094 - val_loss: 7214.4795
Epoch 65/500
37/37 [==============================] - 0s 13ms/step - loss: 2879.2271 - val_loss: 7085.2812
Epoch 66/500
37/37 [==============================] - 0s 13ms/step - loss: 2952.6772 - val_loss: 7230.4976
Epoch 67/500
37/37 [==============================] - 0s 13ms/step - loss: 2893.4966 - val_loss: 7558.4473
Epoch 68/500
37/37 [==============================] - 0s 12ms/step - loss: 2822.8994 - val_loss: 7397.1890
Epoch 69/500
37/37 [==============================] - 0s 12ms/step - loss: 2913.0369 - val_loss: 7179.3315
Epoch 70/500
37/37 [==============================] - 0s 12ms/step - loss: 2791.2000 - val_loss: 7226.1499
Epoch 71/500
37/37 [==============================] - 0s 13ms/step - loss: 2831.6665 - val_loss: 7426.9976
Epoch 72/500
37/37 [==============================] - 0s 13ms/step - loss: 2752.1257 - val_loss: 7136.7891
Epoch 73/500
37/37 [==============================] - 0s 12ms/step - loss: 2838.7649 - val_loss: 7207.7051
Epoch 74/500
37/37 [==============================] - 0s 12ms/step - loss: 2912.6670 - val_loss: 7035.8809
Epoch 75/500
37/37 [==============================] - 0s 12ms/step - loss: 2764.2727 - val_loss: 7195.8140
Epoch 76/500
37/37 [==============================] - 0s 12ms/step - loss: 2765.2517 - val_loss: 6928.9390
Epoch 77/500
37/37 [==============================] - 0s 12ms/step - loss: 2865.9333 - val_loss: 7076.2734
Epoch 78/500
37/37 [==============================] - 0s 12ms/step - loss: 2586.8462 - val_loss: 7364.3418
Epoch 79/500
37/37 [==============================] - 0s 13ms/step - loss: 2708.7432 - val_loss: 7019.3965
Epoch 80/500
37/37 [==============================] - 0s 13ms/step - loss: 2592.9927 - val_loss: 7074.4004
Epoch 81/500
37/37 [==============================] - 0s 13ms/step - loss: 2662.5278 - val_loss: 6944.2056
Epoch 82/500
37/37 [==============================] - 0s 13ms/step - loss: 2717.3337 - val_loss: 7250.3164
Epoch 83/500
37/37 [==============================] - 0s 13ms/step - loss: 2660.5632 - val_loss: 7010.9854
Epoch 84/500
37/37 [==============================] - 0s 12ms/step - loss: 2688.3325 - val_loss: 6941.0669
Epoch 85/500
37/37 [==============================] - 0s 13ms/step - loss: 2695.4004 - val_loss: 7218.8418
Epoch 86/500
37/37 [==============================] - 0s 13ms/step - loss: 2710.6099 - val_loss: 7017.6206
Epoch 87/500
37/37 [==============================] - 0s 12ms/step - loss: 2740.1704 - val_loss: 6959.4785
Epoch 88/500
37/37 [==============================] - 0s 12ms/step - loss: 2604.0662 - val_loss: 7038.6172
Epoch 89/500
37/37 [==============================] - 0s 12ms/step - loss: 2625.0173 - val_loss: 7189.7651
Epoch 90/500
37/37 [==============================] - 0s 13ms/step - loss: 2458.3906 - val_loss: 7063.4688
Epoch 91/500
37/37 [==============================] - 0s 12ms/step - loss: 2635.3440 - val_loss: 7239.4287
Epoch 92/500
37/37 [==============================] - 0s 13ms/step - loss: 2553.1011 - val_loss: 7021.2002
Epoch 93/500
37/37 [==============================] - 0s 13ms/step - loss: 2548.2954 - val_loss: 7131.6914
Epoch 94/500
37/37 [==============================] - 0s 12ms/step - loss: 2495.5188 - val_loss: 7049.4575
Epoch 95/500
37/37 [==============================] - 0s 13ms/step - loss: 2571.1567 - val_loss: 7073.6602
Epoch 96/500
37/37 [==============================] - 0s 13ms/step - loss: 2544.2407 - val_loss: 6875.5815
Epoch 97/500
37/37 [==============================] - 0s 12ms/step - loss: 2505.8708 - val_loss: 7138.2949
Epoch 98/500
37/37 [==============================] - 0s 13ms/step - loss: 2501.5974 - val_loss: 6835.8462
Epoch 99/500
37/37 [==============================] - 0s 13ms/step - loss: 2423.2566 - val_loss: 6964.4429
Epoch 100/500
37/37 [==============================] - 0s 12ms/step - loss: 2556.8601 - val_loss: 6890.1938
Epoch 101/500
37/37 [==============================] - 0s 13ms/step - loss: 2421.1416 - val_loss: 6855.7549
Epoch 102/500
37/37 [==============================] - 0s 12ms/step - loss: 2398.2432 - val_loss: 7644.9922
Epoch 103/500
37/37 [==============================] - 0s 13ms/step - loss: 2419.2710 - val_loss: 6941.9980
Epoch 104/500
37/37 [==============================] - 0s 12ms/step - loss: 2415.7629 - val_loss: 7121.0283
Epoch 105/500
37/37 [==============================] - 0s 13ms/step - loss: 2441.2029 - val_loss: 7098.6611
Epoch 106/500
37/37 [==============================] - 0s 13ms/step - loss: 2461.0339 - val_loss: 7097.2021
Epoch 107/500
37/37 [==============================] - 0s 13ms/step - loss: 2461.7568 - val_loss: 7593.0986
Epoch 108/500
37/37 [==============================] - 0s 12ms/step - loss: 2474.4609 - val_loss: 6885.4146
Epoch 109/500
37/37 [==============================] - 0s 13ms/step - loss: 2427.8657 - val_loss: 7022.5156
Epoch 110/500
37/37 [==============================] - 0s 12ms/step - loss: 2501.1318 - val_loss: 6927.9478
Epoch 111/500
37/37 [==============================] - 0s 13ms/step - loss: 2427.2805 - val_loss: 6926.1558
Epoch 112/500
37/37 [==============================] - 0s 13ms/step - loss: 2362.2734 - val_loss: 7009.8579
Epoch 113/500
37/37 [==============================] - 0s 12ms/step - loss: 2371.7039 - val_loss: 6935.2183
Epoch 114/500
37/37 [==============================] - 0s 13ms/step - loss: 2364.0032 - val_loss: 7197.0620
Epoch 115/500
37/37 [==============================] - 0s 13ms/step - loss: 2359.9558 - val_loss: 6849.2026
Epoch 116/500
37/37 [==============================] - 0s 13ms/step - loss: 2389.4561 - val_loss: 6960.8657
Epoch 117/500
37/37 [==============================] - 0s 13ms/step - loss: 2473.8486 - val_loss: 7287.8145
Epoch 118/500
37/37 [==============================] - 0s 13ms/step - loss: 2295.5676 - val_loss: 6902.2539
Epoch 119/500
37/37 [==============================] - 0s 12ms/step - loss: 2366.3909 - val_loss: 7057.1328
Epoch 120/500
37/37 [==============================] - 0s 13ms/step - loss: 2435.5386 - val_loss: 6802.4751
Epoch 121/500
37/37 [==============================] - 0s 12ms/step - loss: 2339.7507 - val_loss: 6996.7217
Epoch 122/500
37/37 [==============================] - 0s 12ms/step - loss: 2253.5042 - val_loss: 7526.9131
Epoch 123/500
37/37 [==============================] - 0s 13ms/step - loss: 2390.7849 - val_loss: 7102.4854
Epoch 124/500
37/37 [==============================] - 0s 12ms/step - loss: 2289.2866 - val_loss: 7162.0356
Epoch 125/500
37/37 [==============================] - 0s 13ms/step - loss: 2248.8958 - val_loss: 7204.4985
Epoch 126/500
37/37 [==============================] - 0s 13ms/step - loss: 2330.0222 - val_loss: 6858.7510
Epoch 127/500
37/37 [==============================] - 0s 12ms/step - loss: 2244.3250 - val_loss: 7204.6284
Epoch 128/500
37/37 [==============================] - 0s 12ms/step - loss: 2254.7871 - val_loss: 7150.6396
Epoch 129/500
37/37 [==============================] - 0s 12ms/step - loss: 2313.3379 - val_loss: 6822.8721
Epoch 130/500
37/37 [==============================] - 0s 12ms/step - loss: 2384.5967 - val_loss: 6865.2876
Epoch 131/500
37/37 [==============================] - 0s 13ms/step - loss: 2321.3276 - val_loss: 7152.1113
Epoch 132/500
37/37 [==============================] - 0s 13ms/step - loss: 2315.5701 - val_loss: 6997.2725
Epoch 133/500
37/37 [==============================] - 0s 13ms/step - loss: 2308.2175 - val_loss: 7056.1465
Epoch 134/500
37/37 [==============================] - 0s 13ms/step - loss: 2276.6313 - val_loss: 6938.7646
Epoch 135/500
37/37 [==============================] - 0s 12ms/step - loss: 2330.1113 - val_loss: 7291.4194
Epoch 136/500
37/37 [==============================] - 0s 13ms/step - loss: 2251.5503 - val_loss: 6766.2407
Epoch 137/500
37/37 [==============================] - 0s 12ms/step - loss: 2279.4841 - val_loss: 6793.8716
Epoch 138/500
37/37 [==============================] - 0s 13ms/step - loss: 2212.0132 - val_loss: 6758.5483
Epoch 139/500
37/37 [==============================] - 1s 14ms/step - loss: 2259.0181 - val_loss: 6998.7681
Epoch 140/500
37/37 [==============================] - 0s 12ms/step - loss: 2215.2061 - val_loss: 6750.5474
Epoch 141/500
37/37 [==============================] - 0s 13ms/step - loss: 2172.9438 - val_loss: 6985.2646
Epoch 142/500
37/37 [==============================] - 0s 13ms/step - loss: 2256.5732 - val_loss: 7191.6606
Epoch 143/500
37/37 [==============================] - 0s 12ms/step - loss: 2174.8149 - val_loss: 7566.6147
Epoch 144/500
37/37 [==============================] - 0s 12ms/step - loss: 2376.7139 - val_loss: 6998.4102
Epoch 145/500
37/37 [==============================] - 0s 13ms/step - loss: 2210.5029 - val_loss: 7016.2090
Epoch 146/500
37/37 [==============================] - 0s 13ms/step - loss: 2249.2786 - val_loss: 7214.4702
Epoch 147/500
37/37 [==============================] - 0s 13ms/step - loss: 2207.8855 - val_loss: 6800.9941
Epoch 148/500
37/37 [==============================] - 0s 12ms/step - loss: 2237.6750 - val_loss: 7072.6035
Epoch 149/500
37/37 [==============================] - 0s 13ms/step - loss: 2206.8479 - val_loss: 6860.0195
Epoch 150/500
37/37 [==============================] - 0s 13ms/step - loss: 2166.4292 - val_loss: 6808.3032
Epoch 151/500
37/37 [==============================] - 0s 13ms/step - loss: 2226.0325 - val_loss: 6872.6631
Epoch 152/500
37/37 [==============================] - 0s 12ms/step - loss: 2090.8157 - val_loss: 6816.7544
Epoch 153/500
37/37 [==============================] - 0s 13ms/step - loss: 2187.8374 - val_loss: 6857.2515
Epoch 154/500
37/37 [==============================] - 0s 12ms/step - loss: 2245.0049 - val_loss: 7125.5049
Epoch 155/500
37/37 [==============================] - 0s 13ms/step - loss: 2254.5261 - val_loss: 6876.8442
Epoch 156/500
37/37 [==============================] - 0s 13ms/step - loss: 2111.2427 - val_loss: 6830.3203
Epoch 157/500
37/37 [==============================] - 0s 12ms/step - loss: 2155.2837 - val_loss: 6897.3979
Epoch 158/500
37/37 [==============================] - 0s 13ms/step - loss: 2142.8950 - val_loss: 6836.2769
Epoch 159/500
37/37 [==============================] - 0s 12ms/step - loss: 2337.3689 - val_loss: 7034.9663
Epoch 160/500
37/37 [==============================] - 0s 12ms/step - loss: 2190.0359 - val_loss: 6884.9976
Epoch 161/500
37/37 [==============================] - 0s 13ms/step - loss: 2190.5193 - val_loss: 7151.1548
Epoch 162/500
37/37 [==============================] - 0s 13ms/step - loss: 2172.7297 - val_loss: 6716.7173
Epoch 163/500
37/37 [==============================] - 0s 13ms/step - loss: 2200.8921 - val_loss: 7044.9556
Epoch 164/500
37/37 [==============================] - 0s 13ms/step - loss: 2269.6072 - val_loss: 7025.9336
Epoch 165/500
37/37 [==============================] - 0s 13ms/step - loss: 2166.7695 - val_loss: 6715.9541
Epoch 166/500
37/37 [==============================] - 0s 12ms/step - loss: 2163.0486 - val_loss: 6761.0312
Epoch 167/500
37/37 [==============================] - 0s 13ms/step - loss: 2157.4080 - val_loss: 6878.9878
Epoch 168/500
37/37 [==============================] - 0s 13ms/step - loss: 2107.8196 - val_loss: 6834.9834
Epoch 169/500
37/37 [==============================] - 0s 13ms/step - loss: 2052.9907 - val_loss: 7087.0117
Epoch 170/500
37/37 [==============================] - 0s 13ms/step - loss: 2306.2368 - val_loss: 6743.3667
Epoch 171/500
37/37 [==============================] - 0s 13ms/step - loss: 2192.4631 - val_loss: 6956.0396
Epoch 172/500
37/37 [==============================] - 0s 12ms/step - loss: 2156.3503 - val_loss: 6827.2183
Epoch 173/500
37/37 [==============================] - 0s 13ms/step - loss: 2210.3889 - val_loss: 6840.1206
Epoch 174/500
37/37 [==============================] - 0s 13ms/step - loss: 2204.2725 - val_loss: 7569.9126
Epoch 175/500
37/37 [==============================] - 0s 12ms/step - loss: 2221.6260 - val_loss: 6772.6353
Epoch 176/500
37/37 [==============================] - 0s 12ms/step - loss: 2061.9348 - val_loss: 6685.1274
Epoch 177/500
37/37 [==============================] - 0s 12ms/step - loss: 2149.2690 - val_loss: 7288.4658
Epoch 178/500
37/37 [==============================] - 0s 13ms/step - loss: 2165.6543 - val_loss: 7109.8379
Epoch 179/500
37/37 [==============================] - 0s 13ms/step - loss: 2152.0024 - val_loss: 6899.3438
Epoch 180/500
37/37 [==============================] - 1s 14ms/step - loss: 2123.5466 - val_loss: 6750.3799
Epoch 181/500
37/37 [==============================] - 0s 12ms/step - loss: 2142.1731 - val_loss: 6862.2402
Epoch 182/500
37/37 [==============================] - 1s 14ms/step - loss: 2156.5496 - val_loss: 6964.2354
Epoch 183/500
37/37 [==============================] - 0s 13ms/step - loss: 2194.9956 - val_loss: 6688.7490
Epoch 184/500
37/37 [==============================] - 0s 12ms/step - loss: 2126.2163 - val_loss: 6596.5361
Epoch 185/500
37/37 [==============================] - 0s 12ms/step - loss: 2066.3464 - val_loss: 6553.7910
Epoch 186/500
37/37 [==============================] - 0s 12ms/step - loss: 2166.1587 - val_loss: 6658.4468
Epoch 187/500
37/37 [==============================] - 1s 14ms/step - loss: 2084.0410 - val_loss: 6754.6675
Epoch 188/500
37/37 [==============================] - 1s 14ms/step - loss: 2167.7776 - val_loss: 6813.8501
Epoch 189/500
37/37 [==============================] - 0s 12ms/step - loss: 2134.9858 - val_loss: 6693.4346
Epoch 190/500
37/37 [==============================] - 0s 13ms/step - loss: 2203.8887 - val_loss: 7179.4463
Epoch 191/500
37/37 [==============================] - 1s 30ms/step - loss: 2119.8562 - val_loss: 6733.0166
Epoch 192/500
37/37 [==============================] - 1s 28ms/step - loss: 2073.3318 - val_loss: 6935.3076
Epoch 193/500
37/37 [==============================] - 1s 20ms/step - loss: 2108.0295 - val_loss: 6750.2056
Epoch 194/500
37/37 [==============================] - 0s 13ms/step - loss: 2205.0132 - val_loss: 6762.9097
Epoch 195/500
37/37 [==============================] - 1s 14ms/step - loss: 2038.3010 - val_loss: 7101.6885
Epoch 196/500
37/37 [==============================] - 1s 14ms/step - loss: 2085.8191 - val_loss: 6737.7505
Epoch 197/500
37/37 [==============================] - 1s 14ms/step - loss: 2104.1406 - val_loss: 6855.8501
Epoch 198/500
37/37 [==============================] - 0s 13ms/step - loss: 2206.7097 - val_loss: 6966.3970
Epoch 199/500
37/37 [==============================] - 1s 14ms/step - loss: 2205.8215 - val_loss: 6902.6465
Epoch 200/500
37/37 [==============================] - 1s 14ms/step - loss: 2104.1494 - val_loss: 6905.4219
Epoch 201/500
37/37 [==============================] - 0s 12ms/step - loss: 1968.6731 - val_loss: 6763.7827
Epoch 202/500
37/37 [==============================] - 0s 13ms/step - loss: 2059.8711 - val_loss: 6871.6611
Epoch 203/500
37/37 [==============================] - 0s 13ms/step - loss: 2153.0759 - val_loss: 7316.8042
Epoch 204/500
37/37 [==============================] - 0s 12ms/step - loss: 2071.3162 - val_loss: 6751.2812
Epoch 205/500
37/37 [==============================] - 0s 13ms/step - loss: 2089.9302 - val_loss: 7106.4707
Epoch 206/500
37/37 [==============================] - 1s 14ms/step - loss: 2058.0051 - val_loss: 6954.1597
Epoch 207/500
37/37 [==============================] - 0s 13ms/step - loss: 2113.5645 - val_loss: 7215.8867
Epoch 208/500
37/37 [==============================] - 0s 13ms/step - loss: 2020.6345 - val_loss: 6643.1089
Epoch 209/500
37/37 [==============================] - 0s 13ms/step - loss: 2044.6675 - val_loss: 7158.4551
Epoch 210/500
37/37 [==============================] - 0s 13ms/step - loss: 2054.2461 - val_loss: 7341.8740
Epoch 211/500
37/37 [==============================] - 0s 13ms/step - loss: 2083.9915 - val_loss: 6990.6753
Epoch 212/500
37/37 [==============================] - 0s 13ms/step - loss: 2094.6387 - val_loss: 6928.2217
Epoch 213/500
37/37 [==============================] - 0s 12ms/step - loss: 2031.3071 - val_loss: 6796.3042
Epoch 214/500
37/37 [==============================] - 0s 12ms/step - loss: 2075.6365 - val_loss: 6930.2178
Epoch 215/500
37/37 [==============================] - 0s 12ms/step - loss: 2030.5814 - val_loss: 7023.7681
Epoch 216/500
37/37 [==============================] - 0s 13ms/step - loss: 2202.3506 - val_loss: 6885.1431
Epoch 217/500
37/37 [==============================] - 0s 12ms/step - loss: 2021.1178 - val_loss: 6940.5806
Epoch 218/500
37/37 [==============================] - 0s 13ms/step - loss: 2042.7789 - val_loss: 6945.0723
Epoch 219/500
37/37 [==============================] - 0s 13ms/step - loss: 2004.5508 - val_loss: 6913.1167
Epoch 220/500
37/37 [==============================] - 0s 12ms/step - loss: 2026.6282 - val_loss: 7042.6855
Epoch 221/500
37/37 [==============================] - 0s 12ms/step - loss: 2002.9481 - val_loss: 6946.7793
Epoch 222/500
37/37 [==============================] - 0s 13ms/step - loss: 2131.5901 - val_loss: 7281.4414
Epoch 223/500
37/37 [==============================] - 0s 12ms/step - loss: 2044.2643 - val_loss: 6917.5532
Epoch 224/500
37/37 [==============================] - 1s 15ms/step - loss: 2186.0513 - val_loss: 6964.1069
Epoch 225/500
37/37 [==============================] - 1s 27ms/step - loss: 2096.3589 - val_loss: 6851.6631
Epoch 226/500
37/37 [==============================] - 1s 14ms/step - loss: 2040.6884 - val_loss: 6962.1973
Epoch 227/500
37/37 [==============================] - 1s 26ms/step - loss: 2039.7250 - val_loss: 6880.1694
Epoch 228/500
37/37 [==============================] - 1s 14ms/step - loss: 2007.1367 - val_loss: 7072.9653
Epoch 229/500
37/37 [==============================] - 0s 13ms/step - loss: 2009.4509 - val_loss: 6645.9302
Epoch 230/500
37/37 [==============================] - 0s 14ms/step - loss: 2055.3997 - val_loss: 6874.2148
Epoch 231/500
37/37 [==============================] - 0s 13ms/step - loss: 2036.0714 - val_loss: 6993.2368
Epoch 232/500
37/37 [==============================] - 0s 12ms/step - loss: 1977.9169 - val_loss: 6951.8726
Epoch 233/500
37/37 [==============================] - 0s 12ms/step - loss: 1931.2562 - val_loss: 6980.0762
Epoch 234/500
37/37 [==============================] - 0s 13ms/step - loss: 2074.7510 - val_loss: 6890.7554
Epoch 235/500
37/37 [==============================] - 0s 12ms/step - loss: 2020.2158 - val_loss: 6764.9014
Epoch 236/500
37/37 [==============================] - 0s 13ms/step - loss: 1928.8650 - val_loss: 6799.2852
Epoch 237/500
37/37 [==============================] - 0s 12ms/step - loss: 2081.6125 - val_loss: 6840.0376
Epoch 238/500
37/37 [==============================] - 0s 12ms/step - loss: 1907.8896 - val_loss: 6918.4575
Epoch 239/500
37/37 [==============================] - 0s 13ms/step - loss: 2043.4819 - val_loss: 6787.2480
Epoch 240/500
37/37 [==============================] - 0s 13ms/step - loss: 1936.0396 - val_loss: 6869.9419
Epoch 241/500
37/37 [==============================] - 0s 13ms/step - loss: 1932.2242 - val_loss: 6829.7002
Epoch 242/500
37/37 [==============================] - 0s 13ms/step - loss: 2005.7660 - val_loss: 6673.6582
Epoch 243/500
37/37 [==============================] - 0s 13ms/step - loss: 2037.6849 - val_loss: 6809.9429
Epoch 244/500
37/37 [==============================] - 0s 14ms/step - loss: 2078.1836 - val_loss: 6797.8838
Epoch 245/500
37/37 [==============================] - 0s 12ms/step - loss: 2039.9309 - val_loss: 6675.2681
Epoch 246/500
37/37 [==============================] - 1s 14ms/step - loss: 2073.7065 - val_loss: 6773.9536
Epoch 247/500
37/37 [==============================] - 0s 12ms/step - loss: 2100.0559 - val_loss: 6909.3911
Epoch 248/500
37/37 [==============================] - 0s 13ms/step - loss: 1999.8229 - val_loss: 6895.5249
Epoch 249/500
37/37 [==============================] - 0s 13ms/step - loss: 1947.3286 - val_loss: 6882.2710
Epoch 250/500
37/37 [==============================] - 0s 13ms/step - loss: 2000.6821 - val_loss: 6795.7935
Epoch 251/500
37/37 [==============================] - 0s 13ms/step - loss: 2076.6521 - val_loss: 6792.0913
Epoch 252/500
37/37 [==============================] - 0s 13ms/step - loss: 1937.5854 - val_loss: 6865.3418
Epoch 253/500
37/37 [==============================] - 0s 13ms/step - loss: 2050.9392 - val_loss: 6750.1904
Epoch 254/500
37/37 [==============================] - 0s 13ms/step - loss: 1982.9222 - val_loss: 6745.9961
Epoch 255/500
37/37 [==============================] - 0s 13ms/step - loss: 2065.2134 - val_loss: 6978.3335
Epoch 256/500
37/37 [==============================] - 1s 14ms/step - loss: 2002.1820 - val_loss: 6872.8877
Epoch 257/500
37/37 [==============================] - 0s 13ms/step - loss: 2045.4919 - val_loss: 7186.3003
Epoch 258/500
37/37 [==============================] - 0s 13ms/step - loss: 1961.6583 - val_loss: 6706.8340
Epoch 259/500
37/37 [==============================] - 0s 13ms/step - loss: 1980.3260 - val_loss: 6895.0015
Epoch 260/500
37/37 [==============================] - 1s 14ms/step - loss: 1990.0983 - val_loss: 6774.2417
Epoch 261/500
37/37 [==============================] - 0s 13ms/step - loss: 1984.0728 - val_loss: 6830.5947
Epoch 262/500
37/37 [==============================] - 0s 13ms/step - loss: 1992.3618 - val_loss: 6865.8564
Epoch 263/500
37/37 [==============================] - 0s 13ms/step - loss: 1979.0651 - val_loss: 6683.8574
Epoch 264/500
37/37 [==============================] - 0s 13ms/step - loss: 1901.5094 - val_loss: 7450.0449
Epoch 265/500
37/37 [==============================] - 1s 20ms/step - loss: 2072.9148 - val_loss: 6841.5684
Epoch 266/500
37/37 [==============================] - 1s 16ms/step - loss: 1910.5472 - val_loss: 7172.0186
Epoch 267/500
37/37 [==============================] - 1s 21ms/step - loss: 2029.2585 - val_loss: 6834.9644
Epoch 268/500
37/37 [==============================] - 1s 23ms/step - loss: 1927.7936 - val_loss: 7011.8701
Epoch 269/500
37/37 [==============================] - 1s 21ms/step - loss: 1951.3879 - val_loss: 6986.0083
Epoch 270/500
37/37 [==============================] - 1s 16ms/step - loss: 2009.4507 - val_loss: 6785.4053
Epoch 271/500
37/37 [==============================] - 1s 18ms/step - loss: 2043.9478 - val_loss: 6824.9448
Epoch 272/500
37/37 [==============================] - 1s 17ms/step - loss: 1955.3549 - val_loss: 6822.1938
Epoch 273/500
37/37 [==============================] - 1s 16ms/step - loss: 2025.0835 - val_loss: 6790.4331
Epoch 274/500
37/37 [==============================] - 1s 16ms/step - loss: 2019.1550 - val_loss: 6887.2822
Epoch 275/500
37/37 [==============================] - 1s 15ms/step - loss: 2003.5972 - val_loss: 7022.3931
Epoch 276/500
37/37 [==============================] - 1s 18ms/step - loss: 1966.2300 - val_loss: 6810.5215
Epoch 277/500
37/37 [==============================] - 1s 24ms/step - loss: 1964.1376 - val_loss: 6863.2466
Epoch 278/500
37/37 [==============================] - 1s 25ms/step - loss: 1929.8234 - val_loss: 7152.6069
Epoch 279/500
37/37 [==============================] - 1s 26ms/step - loss: 2009.1362 - val_loss: 6678.7969
Epoch 280/500
37/37 [==============================] - 1s 24ms/step - loss: 2004.6903 - val_loss: 6878.0459
Epoch 281/500
37/37 [==============================] - 1s 27ms/step - loss: 1942.2577 - val_loss: 6887.2480
Epoch 282/500
37/37 [==============================] - 1s 26ms/step - loss: 1980.2018 - val_loss: 6843.2573
Epoch 283/500
37/37 [==============================] - 1s 27ms/step - loss: 1937.3331 - val_loss: 7006.1504
Epoch 284/500
37/37 [==============================] - 1s 27ms/step - loss: 1961.9154 - val_loss: 6975.3765
Epoch 285/500
37/37 [==============================] - 1s 23ms/step - loss: 2006.8104 - val_loss: 6796.7319
Epoch 286/500
37/37 [==============================] - 1s 22ms/step - loss: 2056.7146 - val_loss: 6697.9258
Epoch 287/500
37/37 [==============================] - 1s 21ms/step - loss: 1927.2084 - val_loss: 6924.8623
Epoch 288/500
37/37 [==============================] - 1s 31ms/step - loss: 1934.1377 - val_loss: 6717.3110
Epoch 289/500
37/37 [==============================] - 1s 25ms/step - loss: 1981.8225 - val_loss: 6726.1118
Epoch 290/500
37/37 [==============================] - 1s 23ms/step - loss: 1912.7902 - val_loss: 6799.2007
Epoch 291/500
37/37 [==============================] - 1s 22ms/step - loss: 1975.0852 - val_loss: 6708.2007
Epoch 292/500
37/37 [==============================] - 1s 24ms/step - loss: 1960.8704 - val_loss: 6770.6685
Epoch 293/500
37/37 [==============================] - 1s 23ms/step - loss: 1918.3971 - val_loss: 6620.7993
Epoch 294/500
37/37 [==============================] - 1s 20ms/step - loss: 2058.5059 - val_loss: 6852.1328
Epoch 295/500
37/37 [==============================] - 1s 20ms/step - loss: 2024.2202 - val_loss: 6926.3765
Epoch 296/500
37/37 [==============================] - 1s 23ms/step - loss: 1926.3627 - val_loss: 6773.2700
Epoch 297/500
37/37 [==============================] - 1s 21ms/step - loss: 2001.0481 - val_loss: 6663.0762
Epoch 298/500
37/37 [==============================] - 1s 20ms/step - loss: 1939.3434 - val_loss: 6765.2188
Epoch 299/500
37/37 [==============================] - 1s 20ms/step - loss: 1937.7377 - val_loss: 6832.3159
Epoch 300/500
37/37 [==============================] - 1s 21ms/step - loss: 1966.1915 - val_loss: 6755.5259
Epoch 301/500
37/37 [==============================] - 1s 27ms/step - loss: 1941.6926 - val_loss: 6896.2983
Epoch 302/500
37/37 [==============================] - 1s 29ms/step - loss: 1982.9479 - val_loss: 6753.6104
Epoch 303/500
37/37 [==============================] - 1s 17ms/step - loss: 1929.1373 - val_loss: 6874.9492
Epoch 304/500
37/37 [==============================] - 1s 19ms/step - loss: 1933.6860 - val_loss: 6846.0264
Epoch 305/500
37/37 [==============================] - 1s 25ms/step - loss: 2036.7770 - val_loss: 7252.5576
Epoch 306/500
37/37 [==============================] - 1s 20ms/step - loss: 1957.8759 - val_loss: 7228.7158
Epoch 307/500
37/37 [==============================] - 1s 18ms/step - loss: 1950.0631 - val_loss: 7098.7266
Epoch 308/500
37/37 [==============================] - 1s 19ms/step - loss: 2024.3601 - val_loss: 6809.9556
Epoch 309/500
37/37 [==============================] - 1s 22ms/step - loss: 1897.9186 - val_loss: 7082.0239
Epoch 310/500
37/37 [==============================] - 1s 20ms/step - loss: 1977.5172 - val_loss: 6817.7520
Epoch 311/500
37/37 [==============================] - 1s 20ms/step - loss: 1987.2257 - val_loss: 6971.3120
Epoch 312/500
37/37 [==============================] - 1s 19ms/step - loss: 1947.8859 - val_loss: 7341.5410
Epoch 313/500
37/37 [==============================] - 1s 17ms/step - loss: 1935.2534 - val_loss: 6715.9814
Epoch 314/500
37/37 [==============================] - 1s 18ms/step - loss: 1950.1118 - val_loss: 6979.4224
Epoch 315/500
37/37 [==============================] - 1s 19ms/step - loss: 2030.8030 - val_loss: 6772.6074
Epoch 316/500
37/37 [==============================] - 1s 20ms/step - loss: 1843.6193 - val_loss: 7130.2163
Epoch 317/500
37/37 [==============================] - 1s 17ms/step - loss: 1929.4562 - val_loss: 6951.4541
Epoch 318/500
37/37 [==============================] - 1s 19ms/step - loss: 2039.8093 - val_loss: 6776.7520
Epoch 319/500
37/37 [==============================] - 1s 20ms/step - loss: 2019.6320 - val_loss: 6841.7666
Epoch 320/500
37/37 [==============================] - 1s 19ms/step - loss: 1880.6975 - val_loss: 6800.5371
Epoch 321/500
37/37 [==============================] - 1s 18ms/step - loss: 1976.3199 - val_loss: 7038.0806
Epoch 322/500
37/37 [==============================] - 1s 19ms/step - loss: 1967.8767 - val_loss: 6669.4839
Epoch 323/500
37/37 [==============================] - 1s 18ms/step - loss: 1981.6964 - val_loss: 6858.2285
Epoch 324/500
37/37 [==============================] - 1s 17ms/step - loss: 1953.7744 - val_loss: 6818.1040
Epoch 325/500
37/37 [==============================] - 1s 18ms/step - loss: 1894.5153 - val_loss: 6801.4624
Epoch 326/500
37/37 [==============================] - 1s 18ms/step - loss: 1864.4421 - val_loss: 6828.4722
Epoch 327/500
37/37 [==============================] - 1s 18ms/step - loss: 1954.8652 - val_loss: 6787.4575
Epoch 328/500
37/37 [==============================] - 1s 17ms/step - loss: 1901.2119 - val_loss: 6851.7822
Epoch 329/500
37/37 [==============================] - 1s 20ms/step - loss: 2057.7996 - val_loss: 6815.8149
Epoch 330/500
37/37 [==============================] - 1s 18ms/step - loss: 1998.8357 - val_loss: 7082.3125
Epoch 331/500
37/37 [==============================] - 1s 20ms/step - loss: 1923.0940 - val_loss: 6791.3311
Epoch 332/500
37/37 [==============================] - 1s 20ms/step - loss: 1863.4083 - val_loss: 7002.7427
Epoch 333/500
37/37 [==============================] - 1s 18ms/step - loss: 1931.8879 - val_loss: 6791.6313
Epoch 334/500
37/37 [==============================] - 1s 19ms/step - loss: 1965.2068 - val_loss: 6841.8535
Epoch 335/500
37/37 [==============================] - 1s 20ms/step - loss: 1899.4926 - val_loss: 6886.2856
Epoch 336/500
37/37 [==============================] - 1s 19ms/step - loss: 1935.8179 - val_loss: 6831.2412
Epoch 337/500
37/37 [==============================] - 1s 18ms/step - loss: 1910.1562 - val_loss: 7199.0181
Epoch 338/500
37/37 [==============================] - 1s 19ms/step - loss: 1999.0992 - val_loss: 7171.2568
Epoch 339/500
37/37 [==============================] - 1s 19ms/step - loss: 1879.3835 - val_loss: 7023.0723
Epoch 340/500
37/37 [==============================] - 1s 19ms/step - loss: 1917.6912 - val_loss: 6981.4619
Epoch 341/500
37/37 [==============================] - 1s 18ms/step - loss: 1976.2562 - val_loss: 6867.7017
Epoch 342/500
37/37 [==============================] - 1s 19ms/step - loss: 1991.8406 - val_loss: 6780.0508
Epoch 343/500
37/37 [==============================] - 1s 17ms/step - loss: 1880.5175 - val_loss: 6962.7529
Epoch 344/500
37/37 [==============================] - 1s 19ms/step - loss: 1926.0865 - val_loss: 6849.6655
Epoch 345/500
37/37 [==============================] - 1s 19ms/step - loss: 1788.5000 - val_loss: 7245.4268
Epoch 346/500
37/37 [==============================] - 1s 18ms/step - loss: 1962.1354 - val_loss: 7026.1768
Epoch 347/500
37/37 [==============================] - 1s 24ms/step - loss: 1917.0729 - val_loss: 6905.0591
Epoch 348/500
37/37 [==============================] - 1s 25ms/step - loss: 1893.8535 - val_loss: 7092.7397
Epoch 349/500
37/37 [==============================] - 1s 19ms/step - loss: 1995.4403 - val_loss: 6953.7090
Epoch 350/500
37/37 [==============================] - 1s 20ms/step - loss: 1895.4139 - val_loss: 6830.0264
Epoch 351/500
37/37 [==============================] - 1s 21ms/step - loss: 1884.6356 - val_loss: 6862.6787
Epoch 352/500
37/37 [==============================] - 1s 22ms/step - loss: 1959.4052 - val_loss: 6771.3359
Epoch 353/500
37/37 [==============================] - 1s 20ms/step - loss: 1990.5640 - val_loss: 6901.8306
Epoch 354/500
37/37 [==============================] - 1s 19ms/step - loss: 1950.4192 - val_loss: 6787.4380
Epoch 355/500
37/37 [==============================] - 1s 18ms/step - loss: 1783.7998 - val_loss: 6902.6172
Epoch 356/500
37/37 [==============================] - 1s 21ms/step - loss: 1913.8965 - val_loss: 7074.2983
Epoch 357/500
37/37 [==============================] - 1s 19ms/step - loss: 1834.2759 - val_loss: 6861.5840
Epoch 358/500
37/37 [==============================] - 1s 17ms/step - loss: 2042.3873 - val_loss: 6771.4492
Epoch 359/500
37/37 [==============================] - 1s 17ms/step - loss: 1885.4407 - val_loss: 6981.0347
Epoch 360/500
37/37 [==============================] - 1s 17ms/step - loss: 1904.2529 - val_loss: 6751.4604
Epoch 361/500
37/37 [==============================] - 1s 16ms/step - loss: 1887.4980 - val_loss: 6798.0171
Epoch 362/500
37/37 [==============================] - 1s 15ms/step - loss: 1896.6173 - val_loss: 6816.1372
Epoch 363/500
37/37 [==============================] - 1s 16ms/step - loss: 1862.7819 - val_loss: 7348.2544
Epoch 364/500
37/37 [==============================] - 1s 17ms/step - loss: 1901.2867 - val_loss: 6840.6069
Epoch 365/500
37/37 [==============================] - 1s 15ms/step - loss: 1998.0333 - val_loss: 6860.0757
Epoch 366/500
37/37 [==============================] - 1s 16ms/step - loss: 1885.9954 - val_loss: 6717.3672
Epoch 367/500
37/37 [==============================] - 1s 15ms/step - loss: 1930.0421 - val_loss: 6978.5254
Epoch 368/500
37/37 [==============================] - 1s 15ms/step - loss: 1913.1318 - val_loss: 6868.7925
Epoch 369/500
37/37 [==============================] - 1s 16ms/step - loss: 1962.5023 - val_loss: 7151.2671
Epoch 370/500
37/37 [==============================] - 1s 16ms/step - loss: 1864.7449 - val_loss: 6633.7422
Epoch 371/500
37/37 [==============================] - 1s 15ms/step - loss: 1853.1460 - val_loss: 6852.3296
Epoch 372/500
37/37 [==============================] - 1s 15ms/step - loss: 1972.0929 - val_loss: 6874.0342
Epoch 373/500
37/37 [==============================] - 1s 16ms/step - loss: 1923.4215 - val_loss: 6816.0840
Epoch 374/500
37/37 [==============================] - 1s 15ms/step - loss: 2007.2872 - val_loss: 6751.2749
Epoch 375/500
37/37 [==============================] - 1s 15ms/step - loss: 1895.4409 - val_loss: 6732.4727
Epoch 376/500
37/37 [==============================] - 1s 15ms/step - loss: 1783.1335 - val_loss: 6731.9580
Epoch 377/500
37/37 [==============================] - 1s 15ms/step - loss: 1982.3951 - val_loss: 6789.3594
Epoch 378/500
37/37 [==============================] - 1s 15ms/step - loss: 1934.1445 - val_loss: 6826.2446
Epoch 379/500
37/37 [==============================] - 1s 15ms/step - loss: 1792.7677 - val_loss: 7159.3970
Epoch 380/500
37/37 [==============================] - 1s 17ms/step - loss: 1838.6044 - val_loss: 6916.4590
Epoch 381/500
37/37 [==============================] - 1s 15ms/step - loss: 1957.1224 - val_loss: 6857.9629
Epoch 382/500
37/37 [==============================] - 1s 16ms/step - loss: 1896.7285 - val_loss: 7224.8062
Epoch 383/500
37/37 [==============================] - 1s 16ms/step - loss: 1907.8623 - val_loss: 6754.9556
Epoch 384/500
37/37 [==============================] - 1s 16ms/step - loss: 1917.2924 - val_loss: 6991.9800
Epoch 385/500
37/37 [==============================] - 1s 16ms/step - loss: 1970.0168 - val_loss: 6742.8633
Epoch 386/500
37/37 [==============================] - 1s 16ms/step - loss: 1889.6941 - val_loss: 6885.3086
Epoch 387/500
37/37 [==============================] - 1s 16ms/step - loss: 1918.9475 - val_loss: 6774.4263
Epoch 388/500
37/37 [==============================] - 1s 16ms/step - loss: 1908.2896 - val_loss: 6772.4629
Epoch 389/500
37/37 [==============================] - 1s 16ms/step - loss: 1863.8616 - val_loss: 6842.9248
Epoch 390/500
37/37 [==============================] - 1s 15ms/step - loss: 1837.0782 - val_loss: 6690.4175
Epoch 391/500
37/37 [==============================] - 1s 15ms/step - loss: 1905.9458 - val_loss: 6890.0854
Epoch 392/500
37/37 [==============================] - 1s 16ms/step - loss: 1780.0166 - val_loss: 6939.3784
Epoch 393/500
37/37 [==============================] - 1s 16ms/step - loss: 1925.6875 - val_loss: 6795.6636
Epoch 394/500
37/37 [==============================] - 1s 15ms/step - loss: 1955.6873 - val_loss: 6902.0430
Epoch 395/500
37/37 [==============================] - 1s 14ms/step - loss: 1827.8306 - val_loss: 6945.5552
Epoch 396/500
37/37 [==============================] - 1s 15ms/step - loss: 1866.5553 - val_loss: 6765.0967
Epoch 397/500
37/37 [==============================] - 1s 15ms/step - loss: 1894.9624 - val_loss: 6828.5601
Epoch 398/500
37/37 [==============================] - 1s 16ms/step - loss: 1966.7175 - val_loss: 7659.5288
Epoch 399/500
37/37 [==============================] - 1s 16ms/step - loss: 1943.6641 - val_loss: 6822.5522
Epoch 400/500
37/37 [==============================] - 1s 15ms/step - loss: 1911.2784 - val_loss: 6679.8125
Epoch 401/500
37/37 [==============================] - 1s 16ms/step - loss: 1976.6350 - val_loss: 6810.8062
Epoch 402/500
37/37 [==============================] - 1s 16ms/step - loss: 1795.4366 - val_loss: 6802.1230
Epoch 403/500
37/37 [==============================] - 1s 15ms/step - loss: 1934.2875 - val_loss: 6906.7910
Epoch 404/500
37/37 [==============================] - 1s 16ms/step - loss: 1817.3730 - val_loss: 6854.9316
Epoch 405/500
37/37 [==============================] - 1s 16ms/step - loss: 1895.9979 - val_loss: 6775.5889
Epoch 406/500
37/37 [==============================] - 1s 15ms/step - loss: 1951.8813 - val_loss: 6879.6265
Epoch 407/500
37/37 [==============================] - 1s 16ms/step - loss: 1855.8193 - val_loss: 6899.6904
Epoch 408/500
37/37 [==============================] - 1s 16ms/step - loss: 1901.1824 - val_loss: 6833.3555
Epoch 409/500
37/37 [==============================] - 1s 15ms/step - loss: 1913.9766 - val_loss: 6683.7114
Epoch 410/500
37/37 [==============================] - 1s 16ms/step - loss: 1853.6370 - val_loss: 7084.1860
Epoch 411/500
37/37 [==============================] - 1s 17ms/step - loss: 1908.3182 - val_loss: 6725.2485
Epoch 412/500
37/37 [==============================] - 1s 16ms/step - loss: 1847.1663 - val_loss: 6832.3911
Epoch 413/500
37/37 [==============================] - 1s 16ms/step - loss: 1914.3085 - val_loss: 6735.6885
Epoch 414/500
37/37 [==============================] - 1s 16ms/step - loss: 1973.4009 - val_loss: 6804.1885
Epoch 415/500
37/37 [==============================] - 1s 16ms/step - loss: 1863.8263 - val_loss: 6795.4043
Epoch 416/500
37/37 [==============================] - 1s 15ms/step - loss: 1917.4490 - val_loss: 6830.2715
Epoch 417/500
37/37 [==============================] - 1s 16ms/step - loss: 1908.1749 - val_loss: 6815.4849
Epoch 418/500
37/37 [==============================] - 1s 16ms/step - loss: 1926.8865 - val_loss: 6818.7183
Epoch 419/500
37/37 [==============================] - 1s 16ms/step - loss: 1916.6652 - val_loss: 7004.1797
Epoch 420/500
37/37 [==============================] - 1s 16ms/step - loss: 1871.4526 - val_loss: 6777.9336
Epoch 421/500
37/37 [==============================] - 1s 15ms/step - loss: 1827.6809 - val_loss: 6759.7529
Epoch 422/500
37/37 [==============================] - 1s 16ms/step - loss: 1834.4255 - val_loss: 6864.4751
Epoch 423/500
37/37 [==============================] - 1s 15ms/step - loss: 1844.5455 - val_loss: 6822.2515
Epoch 424/500
37/37 [==============================] - 1s 15ms/step - loss: 1859.0708 - val_loss: 6877.4199
Epoch 425/500
37/37 [==============================] - 1s 14ms/step - loss: 1976.3237 - val_loss: 6810.9414
Epoch 426/500
37/37 [==============================] - 1s 15ms/step - loss: 1885.8148 - val_loss: 6913.9790
Epoch 427/500
37/37 [==============================] - 1s 15ms/step - loss: 1900.3741 - val_loss: 6865.6353
Epoch 428/500
37/37 [==============================] - 1s 16ms/step - loss: 1786.1184 - val_loss: 6815.2690
Epoch 429/500
37/37 [==============================] - 1s 15ms/step - loss: 1886.7271 - val_loss: 7246.9014
Epoch 430/500
37/37 [==============================] - 1s 16ms/step - loss: 1883.5200 - val_loss: 6825.6538
Epoch 431/500
37/37 [==============================] - 1s 15ms/step - loss: 1829.2032 - val_loss: 7945.8325
Epoch 432/500
37/37 [==============================] - 1s 15ms/step - loss: 1907.9700 - val_loss: 6764.7671
Epoch 433/500
37/37 [==============================] - 1s 15ms/step - loss: 1858.8044 - val_loss: 6869.2446
Epoch 434/500
37/37 [==============================] - 1s 15ms/step - loss: 1879.5695 - val_loss: 7014.5249
Epoch 435/500
37/37 [==============================] - 1s 15ms/step - loss: 1873.8910 - val_loss: 7173.2251
Epoch 436/500
37/37 [==============================] - 1s 16ms/step - loss: 1849.7262 - val_loss: 6905.8096
Epoch 437/500
37/37 [==============================] - 1s 16ms/step - loss: 1841.3042 - val_loss: 6917.3760
Epoch 438/500
37/37 [==============================] - 1s 16ms/step - loss: 1917.7551 - val_loss: 6850.4209
Epoch 439/500
37/37 [==============================] - 1s 15ms/step - loss: 1889.4868 - val_loss: 7246.2559
Epoch 440/500
37/37 [==============================] - 1s 18ms/step - loss: 1834.6895 - val_loss: 6765.9355
Epoch 441/500
37/37 [==============================] - 1s 14ms/step - loss: 1857.1254 - val_loss: 6883.9985
Epoch 442/500
37/37 [==============================] - 1s 15ms/step - loss: 1817.1649 - val_loss: 6760.5557
Epoch 443/500
37/37 [==============================] - 1s 14ms/step - loss: 1846.9874 - val_loss: 6866.9727
Epoch 444/500
37/37 [==============================] - 1s 14ms/step - loss: 1859.4335 - val_loss: 6920.8120
Epoch 445/500
37/37 [==============================] - 1s 15ms/step - loss: 1810.4120 - val_loss: 6794.7510
Epoch 446/500
37/37 [==============================] - 1s 14ms/step - loss: 1923.1898 - val_loss: 6876.9619
Epoch 447/500
37/37 [==============================] - 1s 14ms/step - loss: 1990.2540 - val_loss: 6965.3726
Epoch 448/500
37/37 [==============================] - 1s 15ms/step - loss: 1875.9086 - val_loss: 6883.4961
Epoch 449/500
37/37 [==============================] - 1s 14ms/step - loss: 1901.4452 - val_loss: 6940.7910
Epoch 450/500
37/37 [==============================] - 1s 15ms/step - loss: 1875.6146 - val_loss: 6865.2847
Epoch 451/500
37/37 [==============================] - 1s 14ms/step - loss: 1816.8157 - val_loss: 6850.8960
Epoch 452/500
37/37 [==============================] - 1s 14ms/step - loss: 1831.4716 - val_loss: 6891.0493
Epoch 453/500
37/37 [==============================] - 1s 14ms/step - loss: 1863.3444 - val_loss: 6874.1108
Epoch 454/500
37/37 [==============================] - 1s 14ms/step - loss: 1868.2053 - val_loss: 6991.4014
Epoch 455/500
37/37 [==============================] - 1s 15ms/step - loss: 1822.5496 - val_loss: 6781.2598
Epoch 456/500
37/37 [==============================] - 1s 14ms/step - loss: 1811.6531 - val_loss: 6917.0425
Epoch 457/500
37/37 [==============================] - 1s 14ms/step - loss: 1905.1055 - val_loss: 6980.8477
Epoch 458/500
37/37 [==============================] - 1s 14ms/step - loss: 1900.2893 - val_loss: 7101.0659
Epoch 459/500
37/37 [==============================] - 1s 14ms/step - loss: 2043.6508 - val_loss: 6815.5840
Epoch 460/500
37/37 [==============================] - 1s 15ms/step - loss: 1818.3248 - val_loss: 6922.6084
Epoch 461/500
37/37 [==============================] - 1s 14ms/step - loss: 1925.3131 - val_loss: 6800.9985
Epoch 462/500
37/37 [==============================] - 0s 13ms/step - loss: 1793.9519 - val_loss: 6877.0938
Epoch 463/500
37/37 [==============================] - 1s 15ms/step - loss: 1844.5763 - val_loss: 6900.3657
Epoch 464/500
37/37 [==============================] - 1s 14ms/step - loss: 1766.0619 - val_loss: 6826.4546
Epoch 465/500
37/37 [==============================] - 1s 14ms/step - loss: 1926.2885 - val_loss: 7078.9766
Epoch 466/500
37/37 [==============================] - 1s 14ms/step - loss: 1819.6041 - val_loss: 6689.2222
Epoch 467/500
37/37 [==============================] - 1s 14ms/step - loss: 1988.3812 - val_loss: 6864.1045
Epoch 468/500
37/37 [==============================] - 1s 14ms/step - loss: 1820.5795 - val_loss: 6737.7651
Epoch 469/500
37/37 [==============================] - 1s 14ms/step - loss: 1802.9866 - val_loss: 6755.0649
Epoch 470/500
37/37 [==============================] - 1s 14ms/step - loss: 1821.7710 - val_loss: 7179.6294
Epoch 471/500
37/37 [==============================] - 1s 14ms/step - loss: 1933.1216 - val_loss: 6799.4590
Epoch 472/500
37/37 [==============================] - 1s 14ms/step - loss: 1780.2198 - val_loss: 6725.8994
Epoch 473/500
37/37 [==============================] - 1s 15ms/step - loss: 1962.7889 - val_loss: 6876.3027
Epoch 474/500
37/37 [==============================] - 1s 14ms/step - loss: 1797.6155 - val_loss: 6746.6094
Epoch 475/500
37/37 [==============================] - 1s 14ms/step - loss: 1950.1436 - val_loss: 6699.7041
Epoch 476/500
37/37 [==============================] - 0s 14ms/step - loss: 1888.2802 - val_loss: 6771.4292
Epoch 477/500
37/37 [==============================] - 1s 15ms/step - loss: 1897.7660 - val_loss: 6843.0840
Epoch 478/500
37/37 [==============================] - 1s 15ms/step - loss: 1878.2981 - val_loss: 6715.3721
Epoch 479/500
37/37 [==============================] - 1s 15ms/step - loss: 1846.8121 - val_loss: 6819.9502
Epoch 480/500
37/37 [==============================] - 1s 15ms/step - loss: 1826.0748 - val_loss: 6986.3438
Epoch 481/500
37/37 [==============================] - 1s 14ms/step - loss: 1927.9307 - val_loss: 6917.6475
Epoch 482/500
37/37 [==============================] - 1s 14ms/step - loss: 1871.2460 - val_loss: 6857.4448
Epoch 483/500
37/37 [==============================] - 1s 15ms/step - loss: 1842.6335 - val_loss: 7140.5171
Epoch 484/500
37/37 [==============================] - 1s 14ms/step - loss: 1917.4915 - val_loss: 7209.5264
Epoch 485/500
37/37 [==============================] - 1s 15ms/step - loss: 1851.0659 - val_loss: 6853.6729
Epoch 486/500
37/37 [==============================] - 1s 14ms/step - loss: 1917.1422 - val_loss: 7075.4644
Epoch 487/500
37/37 [==============================] - 0s 13ms/step - loss: 1836.7406 - val_loss: 7224.3638
Epoch 488/500
37/37 [==============================] - 1s 14ms/step - loss: 1839.8773 - val_loss: 6899.6704
Epoch 489/500
37/37 [==============================] - 1s 14ms/step - loss: 1914.3187 - val_loss: 6877.8296
Epoch 490/500
37/37 [==============================] - 1s 15ms/step - loss: 1883.6682 - val_loss: 7114.0894
Epoch 491/500
37/37 [==============================] - 1s 15ms/step - loss: 1807.6877 - val_loss: 7021.2554
Epoch 492/500
37/37 [==============================] - 1s 14ms/step - loss: 1868.1605 - val_loss: 6845.4268
Epoch 493/500
37/37 [==============================] - 1s 14ms/step - loss: 1889.3314 - val_loss: 6944.0615
Epoch 494/500
37/37 [==============================] - 1s 15ms/step - loss: 1927.7555 - val_loss: 6881.1191
Epoch 495/500
37/37 [==============================] - 1s 14ms/step - loss: 1824.5568 - val_loss: 7275.3120
Epoch 496/500
37/37 [==============================] - 1s 14ms/step - loss: 1828.1230 - val_loss: 6955.6958
Epoch 497/500
37/37 [==============================] - 1s 14ms/step - loss: 1877.5487 - val_loss: 7255.4395
Epoch 498/500
37/37 [==============================] - 1s 14ms/step - loss: 1849.6918 - val_loss: 6884.0601
Epoch 499/500
37/37 [==============================] - 1s 14ms/step - loss: 1868.6597 - val_loss: 6814.1660
Epoch 500/500
37/37 [==============================] - 1s 14ms/step - loss: 1778.1266 - val_loss: 6838.9097


Predts = nn.predict(xtest)


from sklearn.metrics import mean_absolute_error
Predtr = nn.predict(xtrain)
print(mean_absolute_error(ytest, Predts))
print(mean_absolute_error(ytrain, Predtr))

2161.7206661717423
2327.9519294167862


Predts = nn.predict(X)
mean_absolute_error(Y, Predts)

2294.7056767677773


A = pd.read_csv("/content/drive/MyDrive/python/Attrition_Project/Trial 01/HR-Employee-Attrition-Table 1.csv")
A.head()


replacer(A)


sns.heatmap(A.corr())

<matplotlib.axes._subplots.AxesSubplot at 0x7f1d8b90c9d0>


X = A.drop(["Attrition","EmployeeCount","EmployeeNumber",'Over18', 'StandardHours'], axis=1)
Y = A[['Attrition']]


def Univariate(A, figsize,rows,columns):
    import seaborn as sns
    import matplotlib.pyplot as plt
    x = 1
    plt.figure(figsize=figsize)
    for i in A.columns:
        if A[i].dtypes == 'object':
            plt.subplot(rows,columns,x)
            sns.countplot(A[i])
            x = x + 1
        else:
            plt.subplot(rows,columns,x)
            sns.distplot(A[i])
            x = x+1


Univariate(A,(40,40), 5,7)


def Bivariate(A, Y,figsize,rows,columns):
    import seaborn as sns
    import matplotlib.pyplot as plt
    x = 1
    plt.figure(figsize=figsize)
    for i in A.columns:
        if A[i].dtypes == 'object':
            plt.subplot(rows,columns,x)
            sns.boxplot(A[i],A[Y])
            x = x + 1
        else:
            plt.subplot(rows,columns,x)
            sns.scatterplot(A[i],A[Y])
            x = x+1


Bivariate(A,'Attrition',(40,40), 7,5)


import seaborn as sns
import matplotlib.pyplot as plt
sns.countplot(A["Department"], hue="BusinessTravel", data=A)

<matplotlib.axes._subplots.AxesSubplot at 0x7f1d8af37f90>


import pandas as pd
B = pd.read_csv("/content/drive/MyDrive/python/Attrition_Project/Trial 01/HR-Employee-Attrition-Table 1.csv")


B.head()


from google.colab import files


!cp /content/drive/MyDrive/python/Attrition_Project/Trial\ 01/PM8.py /content


from PM8 import replacer, preprocessing
replacer(B)


cat = []
con = []
for i in B.columns:
    if B[i].dtypes == 'object':
        cat.append(i)
    else:
        con.append(i)


d = pd.DataFrame([cat, con]).T
d.columns = ["Cate", "Cont"]
d


pd.crosstab(B.Department, B.Gender)


B[cat][:-7]


for i in B[cat]:
    for j in B[cat]:
        a = pd.crosstab(B[i], B[j])
        print(a)
        print("===================||===================||===================||===================")

BusinessTravel     Non-Travel  Travel_Frequently  Travel_Rarely
BusinessTravel                                                 
Non-Travel                150                  0              0
Travel_Frequently           0                277              0
Travel_Rarely               0                  0           1043
===================||===================||===================||===================
Department         Human Resources  Research & Development  Sales
BusinessTravel                                                   
Non-Travel                       6                      97     47
Travel_Frequently               11                     182     84
Travel_Rarely                   46                     682    315
===================||===================||===================||===================
EducationField     Human Resources  Life Sciences  Marketing  Medical  Other  \
BusinessTravel                                                                 
Non-Travel                       4             63         12       50      7   
Travel_Frequently                6            123         27       86     14   
Travel_Rarely                   17            420        120      328     61   

EducationField     Technical Degree  
BusinessTravel                       
Non-Travel                       14  
Travel_Frequently                21  
Travel_Rarely                    97  
===================||===================||===================||===================
Gender             Female  Male
BusinessTravel                 
Non-Travel             49   101
Travel_Frequently     117   160
Travel_Rarely         422   621
===================||===================||===================||===================
JobRole            Healthcare Representative  Human Resources  \
BusinessTravel                                                  
Non-Travel                                15                4   
Travel_Frequently                         26               10   
Travel_Rarely                             90               38   

JobRole            Laboratory Technician  Manager  Manufacturing Director  \
BusinessTravel                                                              
Non-Travel                            28       12                      13   
Travel_Frequently                     51       13                      29   
Travel_Rarely                        180       77                     103   

JobRole            Research Director  Research Scientist  Sales Executive  \
BusinessTravel                                                              
Non-Travel                         6                  28               39   
Travel_Frequently                 12                  54               59   
Travel_Rarely                     62                 210              228   

JobRole            Sales Representative  
BusinessTravel                           
Non-Travel                            5  
Travel_Frequently                    23  
Travel_Rarely                        55  
===================||===================||===================||===================
MaritalStatus      Divorced  Married  Single
BusinessTravel                              
Non-Travel               44       59      47
Travel_Frequently        63      118      96
Travel_Rarely           220      496     327
===================||===================||===================||===================
Over18                Y
BusinessTravel         
Non-Travel          150
Travel_Frequently   277
Travel_Rarely      1043
===================||===================||===================||===================
OverTime            No  Yes
BusinessTravel             
Non-Travel         115   35
Travel_Frequently  191   86
Travel_Rarely      748  295
===================||===================||===================||===================
BusinessTravel          Non-Travel  Travel_Frequently  Travel_Rarely
Department                                                          
Human Resources                  6                 11             46
Research & Development          97                182            682
Sales                           47                 84            315
===================||===================||===================||===================
Department              Human Resources  Research & Development  Sales
Department                                                            
Human Resources                      63                       0      0
Research & Development                0                     961      0
Sales                                 0                       0    446
===================||===================||===================||===================
EducationField          Human Resources  Life Sciences  Marketing  Medical  \
Department                                                                   
Human Resources                      27             16          0       13   
Research & Development                0            440          0      363   
Sales                                 0            150        159       88   

EducationField          Other  Technical Degree  
Department                                       
Human Resources             3                 4  
Research & Development     64                94  
Sales                      15                34  
===================||===================||===================||===================
Gender                  Female  Male
Department                          
Human Resources             20    43
Research & Development     379   582
Sales                      189   257
===================||===================||===================||===================
JobRole                 Healthcare Representative  Human Resources  \
Department                                                           
Human Resources                                 0               52   
Research & Development                        131                0   
Sales                                           0                0   

JobRole                 Laboratory Technician  Manager  \
Department                                               
Human Resources                             0       11   
Research & Development                    259       54   
Sales                                       0       37   

JobRole                 Manufacturing Director  Research Director  \
Department                                                          
Human Resources                              0                  0   
Research & Development                     145                 80   
Sales                                        0                  0   

JobRole                 Research Scientist  Sales Executive  \
Department                                                    
Human Resources                          0                0   
Research & Development                 292                0   
Sales                                    0              326   

JobRole                 Sales Representative  
Department                                    
Human Resources                            0  
Research & Development                     0  
Sales                                     83  
===================||===================||===================||===================
MaritalStatus           Divorced  Married  Single
Department                                       
Human Resources               16       34      13
Research & Development       224      433     304
Sales                         87      206     153
===================||===================||===================||===================
Over18                    Y
Department                 
Human Resources          63
Research & Development  961
Sales                   446
===================||===================||===================||===================
OverTime                 No  Yes
Department                      
Human Resources          46   17
Research & Development  690  271
Sales                   318  128
===================||===================||===================||===================
BusinessTravel    Non-Travel  Travel_Frequently  Travel_Rarely
EducationField                                                
Human Resources            4                  6             17
Life Sciences             63                123            420
Marketing                 12                 27            120
Medical                   50                 86            328
Other                      7                 14             61
Technical Degree          14                 21             97
===================||===================||===================||===================
Department        Human Resources  Research & Development  Sales
EducationField                                                  
Human Resources                27                       0      0
Life Sciences                  16                     440    150
Marketing                       0                       0    159
Medical                        13                     363     88
Other                           3                      64     15
Technical Degree                4                      94     34
===================||===================||===================||===================
EducationField    Human Resources  Life Sciences  Marketing  Medical  Other  \
EducationField                                                                
Human Resources                27              0          0        0      0   
Life Sciences                   0            606          0        0      0   
Marketing                       0              0        159        0      0   
Medical                         0              0          0      464      0   
Other                           0              0          0        0     82   
Technical Degree                0              0          0        0      0   

EducationField    Technical Degree  
EducationField                      
Human Resources                  0  
Life Sciences                    0  
Marketing                        0  
Medical                          0  
Other                            0  
Technical Degree               132  
===================||===================||===================||===================
Gender            Female  Male
EducationField                
Human Resources        8    19
Life Sciences        240   366
Marketing             69    90
Medical              190   274
Other                 29    53
Technical Degree      52    80
===================||===================||===================||===================
JobRole           Healthcare Representative  Human Resources  \
EducationField                                                 
Human Resources                           0               21   
Life Sciences                            60               13   
Marketing                                 0                0   
Medical                                  48               11   
Other                                     9                3   
Technical Degree                         14                4   

JobRole           Laboratory Technician  Manager  Manufacturing Director  \
EducationField                                                             
Human Resources                       0        6                       0   
Life Sciences                       119       40                      71   
Marketing                             0       14                       0   
Medical                              99       32                      53   
Other                                22        5                       7   
Technical Degree                     19        5                      14   

JobRole           Research Director  Research Scientist  Sales Executive  \
EducationField                                                             
Human Resources                   0                   0                0   
Life Sciences                    36                 133              107   
Marketing                         0                   0              122   
Medical                          35                 103               65   
Other                             4                  17               13   
Technical Degree                  5                  39               19   

JobRole           Sales Representative  
EducationField                          
Human Resources                      0  
Life Sciences                       27  
Marketing                           23  
Medical                             18  
Other                                2  
Technical Degree                    13  
===================||===================||===================||===================
MaritalStatus     Divorced  Married  Single
EducationField                             
Human Resources          7       18       2
Life Sciences          134      271     201
Marketing               34       77      48
Medical                107      210     147
Other                   19       36      27
Technical Degree        26       61      45
===================||===================||===================||===================
Over18              Y
EducationField       
Human Resources    27
Life Sciences     606
Marketing         159
Medical           464
Other              82
Technical Degree  132
===================||===================||===================||===================
OverTime           No  Yes
EducationField            
Human Resources    19    8
Life Sciences     439  167
Marketing         111   48
Medical           332  132
Other              55   27
Technical Degree   98   34
===================||===================||===================||===================
BusinessTravel  Non-Travel  Travel_Frequently  Travel_Rarely
Gender                                                      
Female                  49                117            422
Male                   101                160            621
===================||===================||===================||===================
Department  Human Resources  Research & Development  Sales
Gender                                                    
Female                   20                     379    189
Male                     43                     582    257
===================||===================||===================||===================
EducationField  Human Resources  Life Sciences  Marketing  Medical  Other  \
Gender                                                                      
Female                        8            240         69      190     29   
Male                         19            366         90      274     53   

EducationField  Technical Degree  
Gender                            
Female                        52  
Male                          80  
===================||===================||===================||===================
Gender  Female  Male
Gender              
Female     588     0
Male         0   882
===================||===================||===================||===================
JobRole  Healthcare Representative  Human Resources  Laboratory Technician  \
Gender                                                                       
Female                          51               16                     85   
Male                            80               36                    174   

JobRole  Manager  Manufacturing Director  Research Director  \
Gender                                                        
Female        47                      72                 33   
Male          55                      73                 47   

JobRole  Research Scientist  Sales Executive  Sales Representative  
Gender                                                              
Female                  114              132                    38  
Male                    178              194                    45  
===================||===================||===================||===================
MaritalStatus  Divorced  Married  Single
Gender                                  
Female              117      272     199
Male                210      401     271
===================||===================||===================||===================
Over18    Y
Gender     
Female  588
Male    882
===================||===================||===================||===================
OverTime   No  Yes
Gender            
Female    408  180
Male      646  236
===================||===================||===================||===================
BusinessTravel             Non-Travel  Travel_Frequently  Travel_Rarely
JobRole                                                                
Healthcare Representative          15                 26             90
Human Resources                     4                 10             38
Laboratory Technician              28                 51            180
Manager                            12                 13             77
Manufacturing Director             13                 29            103
Research Director                   6                 12             62
Research Scientist                 28                 54            210
Sales Executive                    39                 59            228
Sales Representative                5                 23             55
===================||===================||===================||===================
Department                 Human Resources  Research & Development  Sales
JobRole                                                                  
Healthcare Representative                0                     131      0
Human Resources                         52                       0      0
Laboratory Technician                    0                     259      0
Manager                                 11                      54     37
Manufacturing Director                   0                     145      0
Research Director                        0                      80      0
Research Scientist                       0                     292      0
Sales Executive                          0                       0    326
Sales Representative                     0                       0     83
===================||===================||===================||===================
EducationField             Human Resources  Life Sciences  Marketing  Medical  \
JobRole                                                                         
Healthcare Representative                0             60          0       48   
Human Resources                         21             13          0       11   
Laboratory Technician                    0            119          0       99   
Manager                                  6             40         14       32   
Manufacturing Director                   0             71          0       53   
Research Director                        0             36          0       35   
Research Scientist                       0            133          0      103   
Sales Executive                          0            107        122       65   
Sales Representative                     0             27         23       18   

EducationField             Other  Technical Degree  
JobRole                                             
Healthcare Representative      9                14  
Human Resources                3                 4  
Laboratory Technician         22                19  
Manager                        5                 5  
Manufacturing Director         7                14  
Research Director              4                 5  
Research Scientist            17                39  
Sales Executive               13                19  
Sales Representative           2                13  
===================||===================||===================||===================
Gender                     Female  Male
JobRole                                
Healthcare Representative      51    80
Human Resources                16    36
Laboratory Technician          85   174
Manager                        47    55
Manufacturing Director         72    73
Research Director              33    47
Research Scientist            114   178
Sales Executive               132   194
Sales Representative           38    45
===================||===================||===================||===================
JobRole                    Healthcare Representative  Human Resources  \
JobRole                                                                 
Healthcare Representative                        131                0   
Human Resources                                    0               52   
Laboratory Technician                              0                0   
Manager                                            0                0   
Manufacturing Director                             0                0   
Research Director                                  0                0   
Research Scientist                                 0                0   
Sales Executive                                    0                0   
Sales Representative                               0                0   

JobRole                    Laboratory Technician  Manager  \
JobRole                                                     
Healthcare Representative                      0        0   
Human Resources                                0        0   
Laboratory Technician                        259        0   
Manager                                        0      102   
Manufacturing Director                         0        0   
Research Director                              0        0   
Research Scientist                             0        0   
Sales Executive                                0        0   
Sales Representative                           0        0   

JobRole                    Manufacturing Director  Research Director  \
JobRole                                                                
Healthcare Representative                       0                  0   
Human Resources                                 0                  0   
Laboratory Technician                           0                  0   
Manager                                         0                  0   
Manufacturing Director                        145                  0   
Research Director                               0                 80   
Research Scientist                              0                  0   
Sales Executive                                 0                  0   
Sales Representative                            0                  0   

JobRole                    Research Scientist  Sales Executive  \
JobRole                                                          
Healthcare Representative                   0                0   
Human Resources                             0                0   
Laboratory Technician                       0                0   
Manager                                     0                0   
Manufacturing Director                      0                0   
Research Director                           0                0   
Research Scientist                        292                0   
Sales Executive                             0              326   
Sales Representative                        0                0   

JobRole                    Sales Representative  
JobRole                                          
Healthcare Representative                     0  
Human Resources                               0  
Laboratory Technician                         0  
Manager                                       0  
Manufacturing Director                        0  
Research Director                             0  
Research Scientist                            0  
Sales Executive                               0  
Sales Representative                         83  
===================||===================||===================||===================
MaritalStatus              Divorced  Married  Single
JobRole                                             
Healthcare Representative        34       61      36
Human Resources                  14       28      10
Laboratory Technician            55      116      88
Manager                          23       56      23
Manufacturing Director           36       67      42
Research Director                23       38      19
Research Scientist               62      122     108
Sales Executive                  69      151     106
Sales Representative             11       34      38
===================||===================||===================||===================
Over18                       Y
JobRole                       
Healthcare Representative  131
Human Resources             52
Laboratory Technician      259
Manager                    102
Manufacturing Director     145
Research Director           80
Research Scientist         292
Sales Executive            326
Sales Representative        83
===================||===================||===================||===================
OverTime                    No  Yes
JobRole                            
Healthcare Representative   94   37
Human Resources             39   13
Laboratory Technician      197   62
Manager                     75   27
Manufacturing Director     106   39
Research Director           57   23
Research Scientist         195   97
Sales Executive            232   94
Sales Representative        59   24
===================||===================||===================||===================
BusinessTravel  Non-Travel  Travel_Frequently  Travel_Rarely
MaritalStatus                                               
Divorced                44                 63            220
Married                 59                118            496
Single                  47                 96            327
===================||===================||===================||===================
Department     Human Resources  Research & Development  Sales
MaritalStatus                                                
Divorced                    16                     224     87
Married                     34                     433    206
Single                      13                     304    153
===================||===================||===================||===================
EducationField  Human Resources  Life Sciences  Marketing  Medical  Other  \
MaritalStatus                                                               
Divorced                      7            134         34      107     19   
Married                      18            271         77      210     36   
Single                        2            201         48      147     27   

EducationField  Technical Degree  
MaritalStatus                     
Divorced                      26  
Married                       61  
Single                        45  
===================||===================||===================||===================
Gender         Female  Male
MaritalStatus              
Divorced          117   210
Married           272   401
Single            199   271
===================||===================||===================||===================
JobRole        Healthcare Representative  Human Resources  \
MaritalStatus                                               
Divorced                              34               14   
Married                               61               28   
Single                                36               10   

JobRole        Laboratory Technician  Manager  Manufacturing Director  \
MaritalStatus                                                           
Divorced                          55       23                      36   
Married                          116       56                      67   
Single                            88       23                      42   

JobRole        Research Director  Research Scientist  Sales Executive  \
MaritalStatus                                                           
Divorced                      23                  62               69   
Married                       38                 122              151   
Single                        19                 108              106   

JobRole        Sales Representative  
MaritalStatus                        
Divorced                         11  
Married                          34  
Single                           38  
===================||===================||===================||===================
MaritalStatus  Divorced  Married  Single
MaritalStatus                           
Divorced            327        0       0
Married               0      673       0
Single                0        0     470
===================||===================||===================||===================
Over18           Y
MaritalStatus     
Divorced       327
Married        673
Single         470
===================||===================||===================||===================
OverTime        No  Yes
MaritalStatus          
Divorced       228   99
Married        487  186
Single         339  131
===================||===================||===================||===================
BusinessTravel  Non-Travel  Travel_Frequently  Travel_Rarely
Over18                                                      
Y                      150                277           1043
===================||===================||===================||===================
Department  Human Resources  Research & Development  Sales
Over18                                                    
Y                        63                     961    446
===================||===================||===================||===================
EducationField  Human Resources  Life Sciences  Marketing  Medical  Other  \
Over18                                                                      
Y                            27            606        159      464     82   

EducationField  Technical Degree  
Over18                            
Y                            132  
===================||===================||===================||===================
Gender  Female  Male
Over18              
Y          588   882
===================||===================||===================||===================
JobRole  Healthcare Representative  Human Resources  Laboratory Technician  \
Over18                                                                       
Y                              131               52                    259   

JobRole  Manager  Manufacturing Director  Research Director  \
Over18                                                        
Y            102                     145                 80   

JobRole  Research Scientist  Sales Executive  Sales Representative  
Over18                                                              
Y                       292              326                    83  
===================||===================||===================||===================
MaritalStatus  Divorced  Married  Single
Over18                                  
Y                   327      673     470
===================||===================||===================||===================
Over18     Y
Over18      
Y       1470
===================||===================||===================||===================
OverTime    No  Yes
Over18             
Y         1054  416
===================||===================||===================||===================
BusinessTravel  Non-Travel  Travel_Frequently  Travel_Rarely
OverTime                                                    
No                     115                191            748
Yes                     35                 86            295
===================||===================||===================||===================
Department  Human Resources  Research & Development  Sales
OverTime                                                  
No                       46                     690    318
Yes                      17                     271    128
===================||===================||===================||===================
EducationField  Human Resources  Life Sciences  Marketing  Medical  Other  \
OverTime                                                                    
No                           19            439        111      332     55   
Yes                           8            167         48      132     27   

EducationField  Technical Degree  
OverTime                          
No                            98  
Yes                           34  
===================||===================||===================||===================
Gender    Female  Male
OverTime              
No           408   646
Yes          180   236
===================||===================||===================||===================
JobRole   Healthcare Representative  Human Resources  Laboratory Technician  \
OverTime                                                                      
No                               94               39                    197   
Yes                              37               13                     62   

JobRole   Manager  Manufacturing Director  Research Director  \
OverTime                                                       
No             75                     106                 57   
Yes            27                      39                 23   

JobRole   Research Scientist  Sales Executive  Sales Representative  
OverTime                                                             
No                       195              232                    59  
Yes                       97               94                    24  
===================||===================||===================||===================
MaritalStatus  Divorced  Married  Single
OverTime                                
No                  228      487     339
Yes                  99      186     131
===================||===================||===================||===================
Over18       Y
OverTime      
No        1054
Yes        416
===================||===================||===================||===================
OverTime    No  Yes
OverTime           
No        1054    0
Yes          0  416
===================||===================||===================||===================


B.columns

Index(['Attrition', 'Age', 'BusinessTravel', 'DailyRate', 'Department',
       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')


from warnings import filterwarnings
filterwarnings("ignore")
Bivariate(B, "Attrition", (40, 40), 7,5)


plt.figure(figsize=(20,20))

sns.heatmap(A.corr(), annot = True, cmap="YlGnBu")

<matplotlib.axes._subplots.AxesSubplot at 0x7f1d8ae47950>


X = B.drop(['Over18', "Attrition", "StandardHours",'EmployeeCount','EmployeeNumber'], axis = 1)
Y = B[["Attrition"]]


X = preprocessing(X)


from sklearn.model_selection import train_test_split
xtrain, xtest,ytrain, ytest = train_test_split(X, Y,test_size=0.2, random_state=40)


from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
dtr = DecisionTreeClassifier(random_state=20,max_depth=2)
abr = AdaBoostClassifier(dtr)
tp = {"n_estimators": range(40)}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(abr,tp, scoring="accuracy", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'n_estimators': 17}


dtr = DecisionTreeClassifier(random_state=20,max_depth=2)
abr = AdaBoostClassifier(dtr, random_state=20, n_estimators=bestval['n_estimators'])
modelr = abr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import accuracy_score
print("train Acc ====>", round(accuracy_score(predtr, ytrain),20))
print("test Acc ====>", round(accuracy_score(predts, ytest),20))

train Acc ====> 0.9090136054421769
test Acc ====> 0.8639455782312924


from sklearn.tree import DecisionTreeClassifier
dtr = DecisionTreeClassifier(random_state=20)
tp = {"max_depth": range(20)}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(dtr,tp, scoring="accuracy", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'max_depth': 5}


dtr = DecisionTreeClassifier(random_state=20,max_depth=bestval["max_depth"])
model = dtr.fit(xtrain,ytrain)
predtr = model.predict(xtrain)
predts = model.predict(xtest)
from sklearn.metrics import accuracy_score
print("train Acc ====>", round(accuracy_score(predtr, ytrain),20))
print("test Acc ====>", round(accuracy_score(predts, ytest),20))

train Acc ====> 0.8937074829931972
test Acc ====> 0.8231292517006803


from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
tp = {"n_estimators": range(40)}
from sklearn.model_selection import GridSearchCV
cv = GridSearchCV(rfc,tp, scoring="accuracy", cv = 5)
cvmodel = cv.fit(xtrain,ytrain)
bestval =cvmodel.best_params_
bestval

{'n_estimators': 28}


rfc  = RandomForestClassifier(random_state=20,n_estimators=bestval['n_estimators'])
modelr = rfc.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import accuracy_score
print("train Acc ====>", round(accuracy_score(predtr, ytrain),20))
print("test Acc ====>", round(accuracy_score(predts, ytest),20))

train Acc ====> 0.9974489795918368
test Acc ====> 0.8707482993197279


from keras.layers import Dense, Dropout
from keras.models import Sequential 
!pip install keras_tuner

Collecting keras_tuner
  Downloading keras_tuner-1.1.0-py3-none-any.whl (98 kB)
     |████████████████████████████████| 98 kB 3.8 MB/s 
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (2.23.0)
Requirement already satisfied: ipython in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (5.5.0)
Collecting kt-legacy
  Downloading kt_legacy-1.0.4-py3-none-any.whl (9.6 kB)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (1.19.5)
Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (21.3)
Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (2.7.0)
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from keras_tuner) (1.4.1)
Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (1.0.18)
Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (2.6.1)
Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (5.1.1)
Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (4.8.0)
Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (0.7.5)
Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (4.4.2)
Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (0.8.1)
Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->keras_tuner) (57.4.0)
Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->keras_tuner) (1.15.0)
Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->keras_tuner) (0.2.5)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->keras_tuner) (3.0.7)
Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython->keras_tuner) (0.7.0)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (2021.10.8)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (2.10)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (1.24.3)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras_tuner) (3.0.4)
Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.43.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (0.37.1)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (0.4.6)
Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (0.6.1)
Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (3.17.3)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.0.1)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.8.1)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (3.3.6)
Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.35.0)
Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras_tuner) (1.0.0)
Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras_tuner) (4.8)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras_tuner) (0.2.8)
Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->keras_tuner) (4.2.4)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras_tuner) (1.3.0)
Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->keras_tuner) (4.10.1)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras_tuner) (3.7.0)
Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard->keras_tuner) (3.10.0.2)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->keras_tuner) (0.4.8)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras_tuner) (3.1.1)
Installing collected packages: kt-legacy, keras-tuner
Successfully installed keras-tuner-1.1.0 kt-legacy-1.0.4


import keras_tuner as kt


def model_builder(hp):
    nn = Sequential()
    nn.add(Dense(1500,input_dim=xtrain.shape[1]))
    x = hp.Int("units",min_value=20,max_value=1600,step=35)
    nn.add(Dense(units=x))
    y = hp.Choice("rate",values=[0.1,0.2,0.3,0.4,0.5,0.6,0.7])
    nn.add(Dropout(rate=y))
    nn.add(Dense(1,activation="sigmoid"))
    nn.compile(loss="binary_crossentropy",metrics="accuracy")
    return nn


import keras_tuner as kt
tuner = kt.Hyperband(model_builder,
            objective="val_accuracy",
            max_epochs=10,
            directory="/content/drive/MyDrive/python/Attrition_Project/AtriionNN4",
            project_name="Keras_TN")


tuner.search(xtrain,ytrain,epochs=500, validation_split=0.2)

Trial 30 Complete [00h 00m 04s]
val_accuracy: 0.8601694703102112

Best val_accuracy So Far: 0.8728813529014587
Total elapsed time: 00h 02m 22s
INFO:tensorflow:Oracle triggered exit


tuner.get_best_hyperparameters()[0].get("units")

195


tuner.get_best_hyperparameters()[0].get("rate")

0.1


from keras.callbacks import EarlyStopping

es = EarlyStopping(patience=25, monitor="val_accuracy")
nn = Sequential()
# nn.add(Dense(650,input_dim=xtrain.shape[1]))
# nn.add(Dense(650, activation="relu"))
# nn.add(Dropout(0.4))
# nn.add(Dense(650, activation="relu"))
# nn.add(Dropout(0.4))
# nn.add(Dense(650, activation="relu"))
# nn.add(Dropout(0.4))
# nn.add(Dense(650, activation="relu"))
nn.add(Dense(195,input_dim=xtrain.shape[1]))
nn.add(Dense(195, activation="relu"))
nn.add(Dropout(0.1))
nn.add(Dense(195, activation="relu"))
nn.add(Dropout(0.1))
nn.add(Dense(195, activation="relu"))
nn.add(Dropout(0.1))
nn.add(Dense(195, activation="relu"))
nn.add(Dense(1,activation="sigmoid"))
nn.compile(optimizer="Adam", loss="binary_crossentropy",metrics="accuracy")


model = nn.fit(xtrain,ytrain,epochs = 100, batch_size=50, validation_split = 0.2)

Epoch 1/100
19/19 [==============================] - 2s 46ms/step - loss: 0.4338 - accuracy: 0.8394 - val_loss: 0.4337 - val_accuracy: 0.8136
Epoch 2/100
19/19 [==============================] - 0s 15ms/step - loss: 0.3304 - accuracy: 0.8511 - val_loss: 0.4253 - val_accuracy: 0.8644
Epoch 3/100
19/19 [==============================] - 0s 15ms/step - loss: 0.2987 - accuracy: 0.8862 - val_loss: 0.4642 - val_accuracy: 0.8559
Epoch 4/100
19/19 [==============================] - 0s 13ms/step - loss: 0.2339 - accuracy: 0.9074 - val_loss: 0.4894 - val_accuracy: 0.8644
Epoch 5/100
19/19 [==============================] - 0s 11ms/step - loss: 0.1976 - accuracy: 0.9277 - val_loss: 0.6329 - val_accuracy: 0.8305
Epoch 6/100
19/19 [==============================] - 0s 15ms/step - loss: 0.1614 - accuracy: 0.9415 - val_loss: 0.6817 - val_accuracy: 0.8390
Epoch 7/100
19/19 [==============================] - 0s 13ms/step - loss: 0.1371 - accuracy: 0.9489 - val_loss: 0.7506 - val_accuracy: 0.8517
Epoch 8/100
19/19 [==============================] - 0s 14ms/step - loss: 0.1166 - accuracy: 0.9553 - val_loss: 0.7291 - val_accuracy: 0.8263
Epoch 9/100
19/19 [==============================] - 0s 13ms/step - loss: 0.0776 - accuracy: 0.9681 - val_loss: 0.8918 - val_accuracy: 0.8178
Epoch 10/100
19/19 [==============================] - 0s 15ms/step - loss: 0.0694 - accuracy: 0.9777 - val_loss: 1.0318 - val_accuracy: 0.8220
Epoch 11/100
19/19 [==============================] - 0s 16ms/step - loss: 0.0832 - accuracy: 0.9691 - val_loss: 0.8690 - val_accuracy: 0.8093
Epoch 12/100
19/19 [==============================] - 0s 12ms/step - loss: 0.0796 - accuracy: 0.9691 - val_loss: 0.9552 - val_accuracy: 0.8517
Epoch 13/100
19/19 [==============================] - 0s 14ms/step - loss: 0.0665 - accuracy: 0.9809 - val_loss: 0.9430 - val_accuracy: 0.8305
Epoch 14/100
19/19 [==============================] - 0s 15ms/step - loss: 0.0368 - accuracy: 0.9862 - val_loss: 1.0330 - val_accuracy: 0.8432
Epoch 15/100
19/19 [==============================] - 0s 15ms/step - loss: 0.0343 - accuracy: 0.9883 - val_loss: 1.1524 - val_accuracy: 0.8305
Epoch 16/100
19/19 [==============================] - 0s 15ms/step - loss: 0.0276 - accuracy: 0.9915 - val_loss: 1.1347 - val_accuracy: 0.8390
Epoch 17/100
19/19 [==============================] - 0s 16ms/step - loss: 0.0460 - accuracy: 0.9862 - val_loss: 1.1966 - val_accuracy: 0.8347
Epoch 18/100
19/19 [==============================] - 0s 19ms/step - loss: 0.0268 - accuracy: 0.9904 - val_loss: 1.3333 - val_accuracy: 0.8432
Epoch 19/100
19/19 [==============================] - 0s 12ms/step - loss: 0.0521 - accuracy: 0.9840 - val_loss: 1.1655 - val_accuracy: 0.8305
Epoch 20/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0292 - accuracy: 0.9915 - val_loss: 1.1946 - val_accuracy: 0.8093
Epoch 21/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0193 - accuracy: 0.9926 - val_loss: 1.4223 - val_accuracy: 0.8347
Epoch 22/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0248 - accuracy: 0.9926 - val_loss: 1.5058 - val_accuracy: 0.8263
Epoch 23/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0075 - accuracy: 0.9989 - val_loss: 1.6694 - val_accuracy: 0.8432
Epoch 24/100
19/19 [==============================] - 0s 9ms/step - loss: 0.0061 - accuracy: 0.9979 - val_loss: 1.6052 - val_accuracy: 0.8347
Epoch 25/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 1.7649 - val_accuracy: 0.8432
Epoch 26/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 1.8633 - val_accuracy: 0.8347
Epoch 27/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0111 - accuracy: 0.9947 - val_loss: 1.8337 - val_accuracy: 0.8178
Epoch 28/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0117 - accuracy: 0.9947 - val_loss: 1.6892 - val_accuracy: 0.8136
Epoch 29/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0326 - accuracy: 0.9894 - val_loss: 1.8067 - val_accuracy: 0.8390
Epoch 30/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0504 - accuracy: 0.9851 - val_loss: 1.6620 - val_accuracy: 0.8475
Epoch 31/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0620 - accuracy: 0.9809 - val_loss: 1.1326 - val_accuracy: 0.8390
Epoch 32/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0282 - accuracy: 0.9926 - val_loss: 1.3409 - val_accuracy: 0.8390
Epoch 33/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0162 - accuracy: 0.9968 - val_loss: 1.2650 - val_accuracy: 0.8136
Epoch 34/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0112 - accuracy: 0.9957 - val_loss: 1.4435 - val_accuracy: 0.8263
Epoch 35/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0117 - accuracy: 0.9968 - val_loss: 1.5952 - val_accuracy: 0.8432
Epoch 36/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0096 - accuracy: 0.9979 - val_loss: 1.6732 - val_accuracy: 0.8390
Epoch 37/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0311 - accuracy: 0.9915 - val_loss: 1.5917 - val_accuracy: 0.8008
Epoch 38/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0294 - accuracy: 0.9894 - val_loss: 1.5910 - val_accuracy: 0.7881
Epoch 39/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0157 - accuracy: 0.9947 - val_loss: 1.4121 - val_accuracy: 0.8263
Epoch 40/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0123 - accuracy: 0.9957 - val_loss: 1.4682 - val_accuracy: 0.8347
Epoch 41/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0104 - accuracy: 0.9968 - val_loss: 1.5037 - val_accuracy: 0.8263
Epoch 42/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.5853 - val_accuracy: 0.8305
Epoch 43/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0034 - accuracy: 0.9989 - val_loss: 1.7850 - val_accuracy: 0.8305
Epoch 44/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0088 - accuracy: 0.9968 - val_loss: 1.7381 - val_accuracy: 0.8220
Epoch 45/100
19/19 [==============================] - 0s 9ms/step - loss: 0.0175 - accuracy: 0.9968 - val_loss: 1.5321 - val_accuracy: 0.8263
Epoch 46/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0103 - accuracy: 0.9968 - val_loss: 1.5762 - val_accuracy: 0.8136
Epoch 47/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 1.0000 - val_loss: 1.6906 - val_accuracy: 0.8305
Epoch 48/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0080 - accuracy: 0.9989 - val_loss: 1.6896 - val_accuracy: 0.8432
Epoch 49/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.9989 - val_loss: 1.6530 - val_accuracy: 0.8263
Epoch 50/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0048 - accuracy: 0.9989 - val_loss: 1.7360 - val_accuracy: 0.8390
Epoch 51/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0028 - accuracy: 0.9989 - val_loss: 1.5915 - val_accuracy: 0.8347
Epoch 52/100
19/19 [==============================] - 0s 9ms/step - loss: 0.0076 - accuracy: 0.9989 - val_loss: 1.8529 - val_accuracy: 0.8220
Epoch 53/100
19/19 [==============================] - 0s 7ms/step - loss: 8.7168e-04 - accuracy: 1.0000 - val_loss: 1.8189 - val_accuracy: 0.8263
Epoch 54/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 1.9814 - val_accuracy: 0.8220
Epoch 55/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0038 - accuracy: 0.9989 - val_loss: 1.9304 - val_accuracy: 0.8220
Epoch 56/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0169 - accuracy: 0.9968 - val_loss: 1.9218 - val_accuracy: 0.8093
Epoch 57/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0055 - accuracy: 0.9979 - val_loss: 1.7886 - val_accuracy: 0.8093
Epoch 58/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0046 - accuracy: 0.9989 - val_loss: 1.9047 - val_accuracy: 0.8347
Epoch 59/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 2.0025 - val_accuracy: 0.8178
Epoch 60/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0036 - accuracy: 0.9989 - val_loss: 1.9175 - val_accuracy: 0.8220
Epoch 61/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0142 - accuracy: 0.9957 - val_loss: 1.8620 - val_accuracy: 0.8432
Epoch 62/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0100 - accuracy: 0.9968 - val_loss: 1.9138 - val_accuracy: 0.8432
Epoch 63/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0071 - accuracy: 0.9968 - val_loss: 1.8130 - val_accuracy: 0.8347
Epoch 64/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0173 - accuracy: 0.9957 - val_loss: 1.6294 - val_accuracy: 0.8263
Epoch 65/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0202 - accuracy: 0.9894 - val_loss: 1.6872 - val_accuracy: 0.7966
Epoch 66/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0287 - accuracy: 0.9915 - val_loss: 1.6303 - val_accuracy: 0.8220
Epoch 67/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0156 - accuracy: 0.9926 - val_loss: 1.6928 - val_accuracy: 0.8178
Epoch 68/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0126 - accuracy: 0.9979 - val_loss: 1.6377 - val_accuracy: 0.8093
Epoch 69/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0053 - accuracy: 0.9979 - val_loss: 1.7017 - val_accuracy: 0.8220
Epoch 70/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0153 - accuracy: 0.9957 - val_loss: 1.7496 - val_accuracy: 0.8136
Epoch 71/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0121 - accuracy: 0.9979 - val_loss: 1.7515 - val_accuracy: 0.8178
Epoch 72/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0182 - accuracy: 0.9936 - val_loss: 1.8580 - val_accuracy: 0.8263
Epoch 73/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0081 - accuracy: 0.9979 - val_loss: 1.7471 - val_accuracy: 0.8305
Epoch 74/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0078 - accuracy: 0.9979 - val_loss: 1.7487 - val_accuracy: 0.8178
Epoch 75/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0051 - accuracy: 0.9989 - val_loss: 1.7671 - val_accuracy: 0.8136
Epoch 76/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0053 - accuracy: 0.9989 - val_loss: 1.8310 - val_accuracy: 0.7966
Epoch 77/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0114 - accuracy: 0.9979 - val_loss: 1.9723 - val_accuracy: 0.8178
Epoch 78/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0138 - accuracy: 0.9957 - val_loss: 1.6880 - val_accuracy: 0.8136
Epoch 79/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0062 - accuracy: 0.9968 - val_loss: 1.6508 - val_accuracy: 0.8347
Epoch 80/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0165 - accuracy: 0.9947 - val_loss: 1.6002 - val_accuracy: 0.8051
Epoch 81/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0108 - accuracy: 0.9957 - val_loss: 1.9660 - val_accuracy: 0.8517
Epoch 82/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0161 - accuracy: 0.9957 - val_loss: 1.6241 - val_accuracy: 0.8475
Epoch 83/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0111 - accuracy: 0.9957 - val_loss: 1.5538 - val_accuracy: 0.8475
Epoch 84/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0071 - accuracy: 0.9979 - val_loss: 1.5194 - val_accuracy: 0.8305
Epoch 85/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 1.7342 - val_accuracy: 0.8390
Epoch 86/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0069 - accuracy: 0.9968 - val_loss: 1.7994 - val_accuracy: 0.8136
Epoch 87/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0031 - accuracy: 0.9989 - val_loss: 2.0103 - val_accuracy: 0.8347
Epoch 88/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0044 - accuracy: 0.9989 - val_loss: 1.9661 - val_accuracy: 0.8136
Epoch 89/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0091 - accuracy: 0.9979 - val_loss: 1.9325 - val_accuracy: 0.8347
Epoch 90/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0156 - accuracy: 0.9947 - val_loss: 1.7740 - val_accuracy: 0.8347
Epoch 91/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0094 - accuracy: 0.9979 - val_loss: 1.6758 - val_accuracy: 0.8390
Epoch 92/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0063 - accuracy: 0.9979 - val_loss: 1.7296 - val_accuracy: 0.8347
Epoch 93/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0031 - accuracy: 0.9989 - val_loss: 1.7447 - val_accuracy: 0.8220
Epoch 94/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0030 - accuracy: 0.9989 - val_loss: 1.9674 - val_accuracy: 0.8475
Epoch 95/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0030 - accuracy: 0.9989 - val_loss: 1.9257 - val_accuracy: 0.8390
Epoch 96/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0049 - accuracy: 0.9979 - val_loss: 2.0655 - val_accuracy: 0.8305
Epoch 97/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0191 - accuracy: 0.9957 - val_loss: 1.6315 - val_accuracy: 0.8347
Epoch 98/100
19/19 [==============================] - 0s 8ms/step - loss: 0.0045 - accuracy: 0.9989 - val_loss: 1.8130 - val_accuracy: 0.8390
Epoch 99/100
19/19 [==============================] - 0s 7ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 1.9108 - val_accuracy: 0.8475
Epoch 100/100
19/19 [==============================] - 0s 7ms/step - loss: 3.6949e-04 - accuracy: 1.0000 - val_loss: 2.0301 - val_accuracy: 0.8475


Prediction = (nn.predict(xtest)>0.5).astype("int32")


Prediction

array([[0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [1],
       [1],
       [0],
       [0],
       [1],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [1],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [1],
       [1],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [0],
       [0],
       [1],
       [0],
       [1],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [0],
       [1],
       [1]], dtype=int32)


from sklearn.metrics import accuracy_score
Predictiontr = (nn.predict(xtrain)>0.5).astype("int32")
print(accuracy_score(ytest,Prediction ))
print(accuracy_score(ytrain,Predictiontr ))

0.8605442176870748
0.9693877551020408


from xgboost import XGBClassifier
xbc = XGBClassifier(n_estimators=2000, learning_rate=0.01, random_state=400)
modelr = xbc.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import accuracy_score
print("train Acc ====>", round(accuracy_score(predtr, ytrain),20))
print("test Acc ====>", round(accuracy_score(predts, ytest),20))

train Acc ====> 0.9778911564625851
test Acc ====> 0.8605442176870748


from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
modelr = gnb.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import accuracy_score
print("train Acc ====>", round(accuracy_score(predtr, ytrain),20))
print("test Acc ====>", round(accuracy_score(predts, ytest),20))

train Acc ====> 0.6879251700680272
test Acc ====> 0.6836734693877551


from sklearn.linear_model import LogisticRegression
lgr = LogisticRegression()
modelr = lgr.fit(xtrain,ytrain)
predtr = modelr.predict(xtrain)
predts = modelr.predict(xtest)
from sklearn.metrics import accuracy_score
print("train Acc ====>", round(accuracy_score(predtr, ytrain),20))
print("test Acc ====>", round(accuracy_score(predts, ytest),20))

train Acc ====> 0.8920068027210885
test Acc ====> 0.8979591836734694


B.columns

Index(['Attrition', 'Age', 'BusinessTravel', 'DailyRate', 'Department',
       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')


C = X[["MonthlyIncome", "HourlyRate"]]
C


from sklearn.cluster import KMeans
k = list(range(1,15))
wcss = []
for i in range(1,15):
  km = KMeans(n_clusters=i)
  model = km.fit(C)
  wcss.append(model.inertia_)


import matplotlib.pyplot as plt
plt.scatter(k,wcss,c="red")
plt.plot(k,wcss,c="blue")
plt.xlabel("Number of clusters - K")
plt.ylabel("Within Clusters Sum of Squares")
plt.title("Elbow Curve")
plt.xticks(range(1,15,1))

([<matplotlib.axis.XTick at 0x7f1d881fecd0>,
  <matplotlib.axis.XTick at 0x7f1d881fec90>,
  <matplotlib.axis.XTick at 0x7f1d881fe8d0>,
  <matplotlib.axis.XTick at 0x7f1d8822cc10>,
  <matplotlib.axis.XTick at 0x7f1d88219090>,
  <matplotlib.axis.XTick at 0x7f1d882196d0>,
  <matplotlib.axis.XTick at 0x7f1d88219c50>,
  <matplotlib.axis.XTick at 0x7f1d88214210>,
  <matplotlib.axis.XTick at 0x7f1d88214710>,
  <matplotlib.axis.XTick at 0x7f1d88214c50>,
  <matplotlib.axis.XTick at 0x7f1d881f90d0>,
  <matplotlib.axis.XTick at 0x7f1d88214690>,
  <matplotlib.axis.XTick at 0x7f1d88214450>,
  <matplotlib.axis.XTick at 0x7f1d881f9850>],
 <a list of 14 Text major ticklabel objects>)


km = KMeans(n_clusters=4)
model = km.fit(C)


B1 = B


B1['clusters'] = model.labels_


B.columns

Index(['Attrition', 'Age', 'BusinessTravel', 'DailyRate', 'Department',
       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager', 'clusters'],
      dtype='object')


B1[B1.clusters == 2][["JobLevel"]]


plt.scatter(B1.MonthlyIncome,B1.HourlyRate,c=B1.clusters)
plt.show()


import pandas as pd
A1 = pd.read_csv("/content/drive/MyDrive/python/Attrition_Project/Trial 01/HR-Employee-Attrition-Table 1.csv")
A1


A = preprocessing(A1)


A1.columns

Index(['Attrition', 'Age', 'BusinessTravel', 'DailyRate', 'Department',
       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')


from sklearn.cluster import AgglomerativeClustering
agc = AgglomerativeClustering(n_clusters=5)
B = A1[['PercentSalaryHike' , 'MonthlyIncome', "JobSatisfaction", "DistanceFromHome"]]
model = agc.fit(B)


B["Cluster"]=model.labels_


from scipy.spatial import distance_matrix
from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt


D = pd.DataFrame(distance_matrix(B.values, B.values))
dendrogram(linkage(D))
plt.show()


A1[A1.Cluster == 1][['EnvironmentSatisfaction']]


B["EnvironmentSatisfaction"]=A1.EnvironmentSatisfaction


B.head()


B["BusinessTravel"]=A1.BusinessTravel


B.head()


B[B.Cluster == 2][['BusinessTravel']]

Dep. Variable:	MonthlyRate	R-squared:	0.033
Model:	OLS	Adj. R-squared:	-0.004
Method:	Least Squares	F-statistic:	0.8831
Date:	Fri, 04 Feb 2022	Prob (F-statistic):	0.689
Time:	13:21:40	Log-Likelihood:	-12077.
No. Observations:	1176	AIC:	2.424e+04
Df Residuals:	1131	BIC:	2.447e+04
Df Model:	44
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	-1.445e+16	1.59e+16	-0.909	0.363	-4.57e+16	1.67e+16
Attrition	55.0975	240.753	0.229	0.819	-417.275	527.470
Age	298.7025	305.460	0.978	0.328	-300.629	898.034
DailyRate	-183.6141	211.315	-0.869	0.385	-598.227	230.999
DistanceFromHome	336.9829	211.419	1.594	0.111	-77.835	751.801
Education	-263.1643	214.022	-1.230	0.219	-683.090	156.761
EnvironmentSatisfaction	486.1152	213.609	2.276	0.023	67.001	905.229
HourlyRate	-22.4649	211.382	-0.106	0.915	-437.209	392.279
JobInvolvement	-235.2686	213.422	-1.102	0.271	-654.016	183.479
JobLevel	391.7885	772.086	0.507	0.612	-1123.094	1906.671
JobSatisfaction	-34.7996	211.295	-0.165	0.869	-449.373	379.774
MonthlyIncome	-960.6465	878.010	-1.094	0.274	-2683.357	762.065
NumCompaniesWorked	63.6222	235.808	0.270	0.787	-399.048	526.292
PercentSalaryHike	-20.4557	329.685	-0.062	0.951	-667.318	626.407
PerformanceRating	-82.4763	338.469	-0.244	0.808	-746.575	581.622
RelationshipSatisfaction	13.4784	211.993	0.064	0.949	-402.465	429.422
StockOptionLevel	-189.4367	284.230	-0.666	0.505	-747.113	368.240
TotalWorkingYears	48.4106	478.635	0.101	0.919	-890.703	987.524
TrainingTimesLastYear	33.2005	215.341	0.154	0.877	-389.312	455.713
WorkLifeBalance	153.3362	211.135	0.726	0.468	-260.924	567.596
YearsAtCompany	-365.8887	456.528	-0.801	0.423	-1261.626	529.848
YearsInCurrentRole	-44.6360	343.224	-0.130	0.897	-718.064	628.791
YearsSinceLastPromotion	163.4164	281.274	0.581	0.561	-388.461	715.294
YearsWithCurrManager	-308.5210	344.059	-0.897	0.370	-983.587	366.545
BusinessTravel_Non-Travel	-1.759e+16	1.94e+16	-0.909	0.363	-5.56e+16	2.04e+16
BusinessTravel_Travel_Frequently	-1.759e+16	1.94e+16	-0.909	0.363	-5.56e+16	2.04e+16
BusinessTravel_Travel_Rarely	-1.759e+16	1.94e+16	-0.909	0.363	-5.56e+16	2.04e+16
Department_Human Resources	1.391e+16	1.53e+16	0.909	0.363	-1.61e+16	4.39e+16
Department_Research & Development	1.391e+16	1.53e+16	0.909	0.363	-1.61e+16	4.39e+16
Department_Sales	1.391e+16	1.53e+16	0.909	0.363	-1.61e+16	4.39e+16
EducationField_Human Resources	-5.063e+15	5.57e+15	-0.909	0.363	-1.6e+16	5.86e+15
EducationField_Life Sciences	-5.063e+15	5.57e+15	-0.909	0.363	-1.6e+16	5.86e+15
EducationField_Marketing	-5.063e+15	5.57e+15	-0.909	0.363	-1.6e+16	5.86e+15
EducationField_Medical	-5.063e+15	5.57e+15	-0.909	0.363	-1.6e+16	5.86e+15
EducationField_Other	-5.063e+15	5.57e+15	-0.909	0.363	-1.6e+16	5.86e+15
EducationField_Technical Degree	-5.063e+15	5.57e+15	-0.909	0.363	-1.6e+16	5.86e+15
Gender_Female	5.565e+15	6.12e+15	0.909	0.363	-6.45e+15	1.76e+16
Gender_Male	5.565e+15	6.12e+15	0.909	0.363	-6.45e+15	1.76e+16
JobRole_Healthcare Representative	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Human Resources	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Laboratory Technician	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Manager	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Manufacturing Director	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Research Director	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Research Scientist	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Sales Executive	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
JobRole_Sales Representative	-3.136e+15	3.45e+15	-0.909	0.363	-9.9e+15	3.63e+15
MaritalStatus_Divorced	-1.414e+16	1.56e+16	-0.909	0.363	-4.47e+16	1.64e+16
MaritalStatus_Married	-1.414e+16	1.56e+16	-0.909	0.363	-4.47e+16	1.64e+16
MaritalStatus_Single	-1.414e+16	1.56e+16	-0.909	0.363	-4.47e+16	1.64e+16
OverTime_No	3.492e+16	3.84e+16	0.909	0.363	-4.04e+16	1.1e+17
OverTime_Yes	3.492e+16	3.84e+16	0.909	0.363	-4.04e+16	1.1e+17

Omnibus:	507.575	Durbin-Watson:	1.939
Prob(Omnibus):	0.000	Jarque-Bera (JB):	62.858
Skew:	0.079	Prob(JB):	2.24e-14
Kurtosis:	1.878	Cond. No.	1.26e+16

Dep. Variable:	MonthlyRate	R-squared:	0.033
Model:	OLS	Adj. R-squared:	-0.001
Method:	Least Squares	F-statistic:	0.9673
Date:	Fri, 04 Feb 2022	Prob (F-statistic):	0.530
Time:	13:21:40	Log-Likelihood:	-12077.
No. Observations:	1176	AIC:	2.424e+04
Df Residuals:	1135	BIC:	2.444e+04
Df Model:	40
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	-1.517e+16	1.55e+16	-0.976	0.329	-4.57e+16	1.53e+16
Attrition	77.2780	240.521	0.321	0.748	-394.638	549.194
Age	340.7013	264.364	1.289	0.198	-177.996	859.399
DailyRate	-201.2777	210.886	-0.954	0.340	-615.049	212.493
DistanceFromHome	328.0889	210.519	1.558	0.119	-84.961	741.139
Education	-280.4090	214.684	-1.306	0.192	-701.631	140.813
EnvironmentSatisfaction	500.2943	213.042	2.348	0.019	82.294	918.295
JobInvolvement	-227.8098	212.324	-1.073	0.284	-644.400	188.781
JobLevel	411.9789	756.104	0.545	0.586	-1071.539	1895.497
JobSatisfaction	-29.2593	209.851	-0.139	0.889	-440.999	382.481
MonthlyIncome	-941.1652	867.009	-1.086	0.278	-2642.285	759.954
NumCompaniesWorked	61.4078	229.389	0.268	0.789	-388.666	511.481
PerformanceRating	-109.6781	217.275	-0.505	0.614	-535.984	316.627
StockOptionLevel	-175.8570	282.903	-0.622	0.534	-730.928	379.214
TrainingTimesLastYear	29.0086	214.816	0.135	0.893	-392.473	450.490
WorkLifeBalance	153.0724	210.677	0.727	0.468	-260.287	566.432
YearsAtCompany	-349.6042	436.995	-0.800	0.424	-1207.012	507.804
YearsInCurrentRole	-48.4651	342.100	-0.142	0.887	-719.684	622.754
YearsSinceLastPromotion	163.2809	280.604	0.582	0.561	-387.280	713.842
YearsWithCurrManager	-302.8820	342.998	-0.883	0.377	-975.864	370.100
BusinessTravel_Non-Travel	3.085e+16	3.16e+16	0.976	0.329	-3.11e+16	9.28e+16
BusinessTravel_Travel_Frequently	3.085e+16	3.16e+16	0.976	0.329	-3.11e+16	9.28e+16
BusinessTravel_Travel_Rarely	3.085e+16	3.16e+16	0.976	0.329	-3.11e+16	9.28e+16
Department_Human Resources	1.822e+16	1.87e+16	0.976	0.329	-1.84e+16	5.48e+16
Department_Research & Development	1.822e+16	1.87e+16	0.976	0.329	-1.84e+16	5.48e+16
Department_Sales	1.822e+16	1.87e+16	0.976	0.329	-1.84e+16	5.48e+16
EducationField_Human Resources	-6.424e+15	6.58e+15	-0.976	0.329	-1.93e+16	6.48e+15
EducationField_Life Sciences	-6.424e+15	6.58e+15	-0.976	0.329	-1.93e+16	6.48e+15
EducationField_Marketing	-6.424e+15	6.58e+15	-0.976	0.329	-1.93e+16	6.48e+15
EducationField_Medical	-6.424e+15	6.58e+15	-0.976	0.329	-1.93e+16	6.48e+15
EducationField_Other	-6.424e+15	6.58e+15	-0.976	0.329	-1.93e+16	6.48e+15
EducationField_Technical Degree	-6.424e+15	6.58e+15	-0.976	0.329	-1.93e+16	6.48e+15
Gender_Female	8.733e+15	8.94e+15	0.976	0.329	-8.81e+15	2.63e+16
Gender_Male	8.733e+15	8.94e+15	0.976	0.329	-8.81e+15	2.63e+16
JobRole_Healthcare Representative	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Human Resources	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Laboratory Technician	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Manager	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Manufacturing Director	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Research Director	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Research Scientist	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Sales Executive	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
JobRole_Sales Representative	-3.339e+15	3.42e+15	-0.976	0.329	-1e+16	3.37e+15
MaritalStatus_Divorced	2.041e+15	2.09e+15	0.976	0.329	-2.06e+15	6.14e+15
MaritalStatus_Married	2.041e+15	2.09e+15	0.976	0.329	-2.06e+15	6.14e+15
MaritalStatus_Single	2.041e+15	2.09e+15	0.976	0.329	-2.06e+15	6.14e+15
OverTime_No	-3.491e+16	3.58e+16	-0.976	0.329	-1.05e+17	3.52e+16
OverTime_Yes	-3.491e+16	3.58e+16	-0.976	0.329	-1.05e+17	3.52e+16

Omnibus:	473.086	Durbin-Watson:	1.943
Prob(Omnibus):	0.000	Jarque-Bera (JB):	61.656
Skew:	0.080	Prob(JB):	4.09e-14
Kurtosis:	1.890	Cond. No.	1.17e+16

Omnibus:	497.152	Durbin-Watson:	1.939
Prob(Omnibus):	0.000	Jarque-Bera (JB):	62.490
Skew:	0.079	Prob(JB):	2.69e-14
Kurtosis:	1.882	Cond. No.	1.60e+16

Omnibus:	503.850	Durbin-Watson:	1.936
Prob(Omnibus):	0.000	Jarque-Bera (JB):	62.736
Skew:	0.079	Prob(JB):	2.38e-14
Kurtosis:	1.880	Cond. No.	1.16e+16

	df	sum_sq	mean_sq	F	PR(>F)
MaritalStatus	2.0	1.240117e-28	6.200583e-29	2.94557	0.052883
Residual	1467.0	3.088114e-26	2.105054e-29	NaN	NaN

	Cate	Cont
0	BusinessTravel	Attrition
1	Department	Age
2	EducationField	DailyRate
3	Gender	DistanceFromHome
4	JobRole	Education
5	MaritalStatus	EmployeeCount
6	Over18	EmployeeNumber
7	OverTime	EnvironmentSatisfaction
8	None	HourlyRate
9	None	JobInvolvement
10	None	JobLevel
11	None	JobSatisfaction
12	None	MonthlyIncome
13	None	MonthlyRate
14	None	NumCompaniesWorked
15	None	PercentSalaryHike
16	None	PerformanceRating
17	None	RelationshipSatisfaction
18	None	StandardHours
19	None	StockOptionLevel
20	None	TotalWorkingYears
21	None	TrainingTimesLastYear
22	None	WorkLifeBalance
23	None	YearsAtCompany
24	None	YearsInCurrentRole
25	None	YearsSinceLastPromotion
26	None	YearsWithCurrManager

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

Gender	Female	Male
Department
Human Resources	20	43
Research & Development	379	582
Sales	189	257

	JobLevel
0	2
2	1
5	1
6	1
7	1
9	2
10	1
13	1
16	1
17	1
20	2
21	1
23	1
24	1
26	1
30	1
32	1
35	1
36	1
37	1
38	1
40	1
46	2
47	1
48	2
50	3
52	2
63	3
66	2
68	1
69	1
71	1
72	1
73	2
79	2
83	2
84	2
87	1
88	2
91	2
94	2
99	2
101	1
102	1
104	2
111	2
115	3
120	1
121	2
122	1
124	2
128	1
129	2
130	1
132	2
133	3
135	2
141	1
143	1
145	1
148	1
149	1
151	3
157	1
159	1
160	1
164	1
171	1
173	2
175	1
176	1
179	1
180	1
181	1
182	1
183	1
185	1
191	1
198	2
201	2
203	2
204	2
207	1
211	3
214	1
217	1
224	2
225	1
228	3
229	1
230	1
240	1
241	2
247	2
248	1
249	2
253	2
258	1
259	1
260	1
262	2
264	1
265	2
267	2
272	1
273	2
274	1
278	2
281	2
287	2
289	1
292	1
297	3
298	1
299	2
301	1
302	2
303	2
306	2
309	1
311	3
317	2
318	1
320	2
321	2
322	2
323	1
330	2
331	2
334	2
336	1
345	1
346	2
347	2
351	1
354	2
356	2
357	1
360	2
364	1
368	2
370	1
372	2
373	1
374	2
376	2
377	1
378	2
380	2
381	1
382	1
386	1
388	1
391	2
393	2
396	2
397	2
398	2
399	1
402	2
404	2
405	1
407	1
409	2
412	2
413	2
418	1
421	1
423	3
433	3
438	3
441	1
443	1
447	2
450	2
457	1
461	2
462	2
463	1
467	3
468	2
474	1
475	2
476	1
480	1
481	1
484	2
485	1
487	1
488	2
491	2
495	1
498	1
500	2
501	1
504	2
505	1
508	2
514	1
519	1
521	2
522	1
523	1
526	2
530	3
536	2
540	1
542	3
545	2
546	1
549	2
553	1
555	1
556	2
558	2
559	1
565	1
566	2
569	3
571	1
572	2
574	1
575	2
576	2
577	1
578	2
579	1
580	1
583	2
587	2
591	2
594	1
600	2
603	1
604	2
606	1
612	2
613	1
614	1
622	2
623	1
626	2
628	2
633	1
634	2
636	1
638	2
642	1
643	2
656	1
659	2
664	2
668	1
669	1
670	1
671	1
672	2
673	1
682	1
687	1
689	1
692	2
697	1
698	2
703	2
704	3
708	2
711	1
713	1
715	2
719	2
722	1
726	2
727	1
731	1
734	1
735	2
748	2
751	2
754	1
756	1
761	2
763	1
764	1
768	2
769	1
778	2
780	3
786	1
795	2
801	2
807	3
808	1
809	3
815	1
816	2
817	2
818	1
819	1
822	2
825	2
828	1
829	2
831	1
834	2
835	1
842	1
843	1
844	2
845	2
847	2
849	2
850	1
854	1
856	1
857	1
859	1
862	1
863	1
864	1
868	1
870	2
871	1
872	2
878	2
880	1
884	2
891	1
892	1
893	1
895	2
896	2
901	1
902	1
903	2
906	1
908	3
911	1
917	2
924	1
925	2
928	3
931	2
933	1
938	1
939	2
940	1
953	1
957	1
958	3
959	2
960	2
961	2
964	2
965	1
967	1
968	2
970	1
972	1
977	1
978	2
979	2
980	1
981	2
983	2
985	2
988	2
993	2
995	2
996	2
1001	1
1003	1
1004	1
1006	2
1007	3
1013	2
1015	1
1017	1
1019	2
1020	1
1021	1
1022	1
1023	1
1027	1
1028	1
1029	2
1033	3
1035	1
1036	1
1038	2
1041	2
1042	1
1044	2
1045	1
1048	2
1049	2
1050	1
1051	2
1053	2
1056	1
1059	1
1060	1
1061	1
1063	2
1066	1
1067	2
1069	1
1072	1
1073	2
1074	2
1077	1
1081	3
1082	2
1083	1
1088	1
1092	1
1095	2
1098	2
1099	3
1100	1
1101	2
1102	1
1103	2
1104	1
1107	2
1108	1
1110	1
1112	2
1118	1
1120	2
1121	2
1122	1
1125	2
1128	2
1131	2
1132	2
1133	1
1137	1
1145	2
1147	1
1148	2
1149	1
1152	1
1153	1
1155	2
1158	2
1159	2
1163	1
1164	3
1169	1
1170	1
1171	1
1174	2
1175	2
1179	2
1180	1
1183	2
1187	2
1189	2
1191	2
1192	1
1196	3
1199	2
1201	1
1202	1
1204	1
1205	1
1206	1
1207	1
1210	1
1213	1
1214	3
1215	1
1216	2
1227	1
1229	2
1230	1
1234	2
1236	2
1240	2
1244	1
1246	1
1248	1
1250	2
1251	3
1252	1
1254	2
1255	3
1256	1
1257	3
1258	1
1265	2
1266	1
1267	2
1269	1
1273	1
1274	2
1278	3
1279	1
1280	2
1281	2
1282	1
1283	1
1285	2
1286	1
1287	2
1288	2
1289	1
1290	2
1292	2
1294	2
1297	1
1300	2
1302	1
1305	2
1307	1
1308	2
1312	1
1314	2
1315	2
1316	2
1319	2
1322	3
1324	2
1325	1
1329	1
1332	1
1334	1
1336	1
1337	1
1338	1
1339	1
1341	2
1344	2
1346	2
1347	2
1349	1
1350	2
1352	2
1353	1
1354	1
1358	2
1359	2
1368	2
1371	2
1372	2
1376	1
1378	2
1380	2
1383	1
1386	1
1387	2
1397	2
1400	1
1406	2
1408	1
1409	2
1410	2
1411	1
1412	1
1414	3
1416	2
1418	2
1420	1
1425	2
1427	1
1428	1
1433	2
1439	3
1440	2
1444	1
1446	2
1447	2
1449	1
1451	2
1452	2
1453	2
1456	2
1457	1
1460	1
1467	2
1469	2

	PercentSalaryHike
0	11
1	23
2	15
3	11
4	12
5	13
6	20
7	22
9	13
10	13
11	12
12	17
13	11
14	14
16	12
17	13
19	11
20	18
21	23
23	14
24	11
26	22
27	11
30	11
31	13
32	13
33	14
34	16
35	12
36	14
37	13
38	14
39	19
40	12
41	13
42	12
44	23
46	20
47	21
48	21
49	19
50	13
51	13
52	21
54	19
57	15
58	22
59	18
60	22
61	11
66	13
68	13
69	17
70	11
71	23
72	17
73	17
74	12
75	23
76	14
79	22
80	12
81	18
83	12
84	15
86	13
87	23
88	19
91	22
92	12
94	16
96	21
97	15
99	12
100	22
101	13
102	18
103	14
104	14
107	11
108	11
109	15
111	24
113	12
114	15
118	22
120	25
121	13
122	18
124	18
125	19
127	19
128	14
129	14
130	12
132	11
134	23
135	20
137	14
139	19
140	22
141	13
142	20
143	14
144	17
145	14
146	13
148	21
149	14
150	11
152	20
153	18
155	13
156	17
157	20
159	18
160	24
161	18
162	11
164	15
166	12
169	20
170	16
171	21
172	12
173	11
174	11
175	18
176	14
177	22
179	12
180	23
181	15
182	22
183	15
184	11
185	13
191	16
192	13
193	14
195	12
196	11
197	16
198	14
199	18
200	19
201	15
202	17
203	15
204	19
206	16
207	13
208	14
214	11
216	15
217	14
219	14
220	16
221	21
224	12
225	17
226	17
229	13
230	15
232	17
234	13
236	20
238	11
239	14
240	14
241	18
242	16
243	17
246	13
247	13
248	14
249	17
252	19
253	13
254	14
255	12
256	11
258	11
259	11
260	20
261	18
262	13
264	11
265	19
266	21
267	12
269	11
272	23
273	14
274	17
277	24
278	20
281	25
282	18
283	19
284	13
285	12
286	22
287	14
288	13
289	21
291	12
292	11
293	12
294	12
296	13
298	18
299	18
301	12
302	19
303	11
305	15
306	14
308	13
309	12
310	12
311	18
312	18
315	19
317	15
318	12
319	11
320	11
322	16
323	13
324	13
327	18
328	14
330	11
331	13
332	12
334	14
336	11
337	17
338	13
339	18
340	16
345	12
346	15
347	19
349	14
350	11
351	13
353	13
354	12
355	17
356	23
357	11
358	15
360	11
361	13
362	24
363	13
364	20
365	14
368	12
369	16
370	15
371	16
372	19
373	11
374	16
376	11
377	13
378	13
380	12
381	18
382	22
383	13
385	23
386	12
387	12
388	16
389	12
391	22
393	15
394	12
395	20
396	14
397	11
398	15
399	12
402	11
404	12
405	13
407	21
409	11
410	20
412	14
413	11
414	16
415	16
416	23
418	22
419	15
421	16
422	12
426	14
428	13
430	15
431	11
432	12
436	13
437	14
441	17
443	16
444	14
446	14
447	11
449	11
450	13
452	19
453	11
454	12
457	14
459	17
460	12
461	16
462	12
463	18
468	12
469	12
470	13
472	12
474	11
475	20
476	21
478	11
479	16
480	18
481	13
482	17
483	14
484	22
485	12
487	13
488	13
490	17
491	13
493	11
494	18
495	11
496	11
498	20
500	12
501	20
503	12
504	14
505	13
507	14
508	13
512	13
513	11
514	11
515	18
516	16
517	22
518	20
519	18
521	20
522	17
523	11
525	14
526	11
527	12
528	14
532	12
536	14
539	15
540	13
543	14
545	23
546	11
547	12
548	18
549	11
550	14
551	15
553	20
554	19
555	14
556	17
557	11
558	15
559	13
560	14
562	13
563	23
564	13
565	18
566	12
567	22
570	18
571	14
572	12
573	17
574	16
575	11
576	19
577	13
578	12
579	11
580	16
581	21
582	24
583	17
585	21
586	11
587	20
589	11
591	15
593	15
594	24
596	13
597	13
598	17
599	13
600	12
601	14
602	24
603	14
604	13
605	15
606	16
608	16
612	20
613	19
614	14
615	11
617	12
618	13
619	22
620	11
621	14
622	11
623	12
626	12
628	19
629	13
630	22
631	24
632	14
633	21
634	18
636	19
637	12
638	12
639	16
640	17
641	14
642	19
643	13
644	19
645	19
648	15
650	13
651	22
654	11
655	11
656	24
657	14
658	13
659	14
660	14
661	21
662	13
663	19
664	17
665	18
666	19
667	13
668	18
669	21
670	19
671	14
672	17
673	11
675	15
676	25
678	17
679	13
680	18
682	20
683	18
685	12
686	13
687	21
688	13
689	19
690	11
691	14
692	12
694	14
696	12
697	15
698	16
700	24
703	13
707	19
708	13
709	22
711	20
712	18
713	14
715	13
717	14
718	15
719	17
720	11
722	17
724	15
725	24
726	12
727	15
731	15
732	17
733	12
734	15
735	13
737	14
739	18
740	20
742	18
744	15
745	14
747	12
748	18
751	12
752	16
754	19
756	12
759	16
761	14
762	14
763	19
764	22
765	16
767	15
769	13
772	19
776	14
777	13
778	23
779	12
781	16
783	13
786	19
788	13
792	22
793	16
795	14
796	15
797	20
798	20
800	15
801	14
802	17
803	17
805	13
808	22
815	11
816	15
818	12
819	17
820	11
822	11
823	14
824	19
825	15
826	13
827	14
828	12
830	11
831	12
832	13
833	13
834	20
835	17
839	13
840	12
841	22
842	11
843	22
844	18
845	13
847	20
848	13
849	13
850	12
852	13
853	25
854	19
855	13
856	12
857	11
859	18
860	11
862	13
863	13
864	17
865	19
866	12
868	12
871	13
872	12
873	14
874	13
875	25
876	17
878	16
879	18
880	20
881	14
883	13
884	12
885	14
886	21
889	14
891	13
892	25
893	15
895	12
896	19
897	17
900	12
901	11
902	11
903	11
906	17
909	12
910	22
911	14
912	20
915	20
917	12
920	13
921	11
923	11
924	14
925	16
927	11
929	12
930	14
931	18
932	13
933	11
934	18
935	15
938	12
939	18
940	13
941	12
943	12
944	11
949	11
951	13
952	11
953	12
957	16
959	13
960	17
961	11
963	15
964	12
965	11
967	22
968	16
970	14
972	15
973	12
974	20
977	20
978	20
979	13
980	14
981	18
982	11
983	11
984	11
985	15
986	12
988	14
989	15
991	13
993	11
995	14
996	11
997	13
998	12
1000	13
1001	18
1003	11
1004	12
1006	20
1012	12
1013	14
1015	14
1016	12
1017	14
1018	16
1019	13
1020	12
1021	12
1022	14
1023	22
1025	12
1027	17
1028	12
1029	11
1032	23
1035	18
1036	13
1038	11
1039	15
1042	14
1044	14
1045	14
1046	13
1047	14
1048	18
1049	15
1050	13
1051	12
1052	13
1053	24
1056	15
1057	11
1058	23
1059	13
1060	11
1061	13
1064	21
1065	16
1066	11
1067	17
1068	11
1069	14
1070	14
1071	25
1072	13
1073	14
1074	17
1077	13
1082	14
1083	14
1085	12
1087	25
1088	14
1089	17
1091	13
1092	20
1094	12
1095	12
1097	14
1098	18
1100	23
1101	12
1102	21
1103	14
1104	18
1105	13
1107	11
1108	19
1110	12
1112	11
1113	14
1114	12
1115	13
1117	17
1118	21
1120	12
1121	13
1122	18
1123	16
1127	16
1128	13
1130	17
1131	14
1132	15
1133	12
1134	18
1136	17
1137	14
1139	17
1141	12
1142	14
1143	15
1144	13
1145	12
1146	13
1147	14
1148	13
1149	11
1150	11
1151	21
1152	18
1153	12
1155	15
1157	12
1158	17
1159	13
1160	19
1163	11
1165	11
1167	14
1168	13
1169	17
1170	13
1171	14
1172	11
1174	17
1175	21
1178	19
1179	12
1180	11
1182	15
1183	20
1186	24
1187	15
1188	13
1189	14
1190	13
1191	11
1192	16
1193	22
1197	11
1198	25
1199	14
1200	18
1201	11
1202	19
1203	15
1204	11
1205	12
1206	20
1207	12
1208	14
1210	18
1212	12
1213	21
1215	14
1217	16
1219	17
1220	15
1222	11
1224	15
1226	13
1227	14
1228	19
1229	16
1230	13
1231	14
1232	16
1233	12
1234	11
1236	13
1237	15
1238	13
1239	20
1240	15
1244	16
1245	14
1246	11
1248	11
1249	13
1250	12
1252	14
1253	15
1254	12
1256	14
1258	13
1259	16
1260	14
1261	16
1262	16
1263	22
1265	15
1266	20
1267	14
1269	13
1270	12
1271	13
1272	11
1273	17
1274	14
1276	14
1279	12
1280	13
1281	18
1282	19
1283	11
1286	17
1287	18
1288	14
1289	13
1290	17
1291	15
1292	18
1293	13
1294	12
1297	11
1299	17
1300	21
1302	16
1304	12
1305	15
1307	25
1308	20
1309	13
1311	16
1312	17
1313	15
1314	22
1315	22
1316	20
1317	14
1318	13
1319	13
1320	22
1321	12
1323	15
1324	17
1325	13
1328	21
1329	11
1332	24
1334	19
1335	14
1336	20
1337	19
1338	13
1339	23
1340	13
1341	11
1343	14
1344	22
1345	11
1346	13
1347	21
1349	13
1350	14
1352	15
1353	23
1354	16
1355	16
1356	21
1358	11
1360	12
1361	18
1362	12
1363	14
1364	12
1365	17
1366	19
1367	11
1368	15
1370	18
1371	16
1372	25
1373	16
1375	14
1376	19
1378	12
1379	12
1380	16
1381	14
1382	13
1383	22
1386	13
1387	12
1388	18
1389	21
1390	12
1391	14
1392	11
1393	14
1395	11
1397	11
1398	20
1400	11
1402	11
1404	12
1406	19
1407	12
1408	13
1409	11
1410	14
1411	14
1412	13
1413	19
1415	12
1416	15
1417	19
1418	11
1419	17
1420	22
1422	11
1423	12
1424	19
1425	13
1426	13
1427	19
1428	14
1429	13
1433	22
1434	15
1435	12
1436	11
1438	19
1440	13
1441	21
1442	14
1444	11
1446	21
1447	24
1449	14
1451	11
1452	12
1453	13
1454	15
1455	14
1456	14
1457	14
1458	12
1459	13
1460	14
1464	18
1465	17
1467	20
1468	14
1469	12

	Attrition	Age	DailyRate	DistanceFromHome	Education	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Research & Development	Department_Sales	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	OverTime_No	OverTime_Yes
0	2.280906	0.446350	0.742527	-1.010909	-0.891688	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	2.125136	-1.150554	-0.426230	-1.584178	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834	0	0	1	0	1	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1
1	-0.438422	1.322365	-1.297775	-0.147150	-1.868426	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	-0.678049	2.129306	2.346151	1.191438	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541	0	1	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	1	0
2	2.280906	0.008343	1.414363	-0.887515	-0.891688	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	1.324226	-0.057267	-0.426230	-0.658973	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935	0	0	1	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1
3	-0.438422	-0.429664	1.461466	-0.764121	1.061787	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	-0.678049	-1.150554	-0.426230	0.266233	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935	0	1	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1
4	-0.438422	-1.086676	-0.524295	-0.887515	-1.868426	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	2.525591	-0.877232	-0.426230	1.191438	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227	0	0	1	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	1	0
5	-0.438422	-0.539166	0.502054	-0.887515	-0.891688	1.169781	0.645041	0.379672	-0.961486	1.153254	-0.729850	-1.078504	-0.603911	-0.426230	0.266233	-0.932014	-0.421642	-0.620189	-1.077862	-0.001333	0.764998	0.252146	0.526188	0	1	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	1	0
6	-0.438422	2.417384	1.292887	-0.764121	0.085049	0.254625	0.743454	1.785511	-0.961486	-1.567907	-0.814416	0.523316	1.309341	2.346151	-1.584178	2.589994	0.092620	0.155707	-1.077862	-0.981014	-1.167687	-0.679146	-1.155935	0	0	1	1	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	1
7	-0.438422	-0.758170	1.377177	1.827158	-1.868426	1.169781	0.054562	0.379672	-0.961486	0.246200	-0.809529	-0.678049	1.855984	2.346151	-0.658973	0.241988	-1.321601	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935	0	0	1	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	1	0
8	-0.438422	0.117845	-1.453958	1.703764	0.085049	1.169781	-1.077188	-1.026167	0.845911	0.246200	0.642338	-1.078504	1.582663	2.346151	-0.658973	-0.932014	-0.164511	-0.620189	0.338096	0.325228	0.764998	-0.368715	1.086895	0	1	0	1	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	1	0
9	-0.438422	-0.101159	1.230910	2.197341	0.085049	0.254625	1.383138	0.379672	-0.057788	0.246200	-0.268983	1.324226	-0.603911	-0.426230	-0.658973	1.415991	0.735447	0.155707	-1.077862	-0.001333	0.764998	1.493867	0.806541	0	0	1	1	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	1	0
10	-0.438422	-0.210661	0.016150	0.840004	0.085049	-1.575686	0.891073	1.785511	-0.961486	-0.660853	-0.866261	-1.078504	-0.603911	-0.426230	0.266233	0.241988	-0.678774	1.707500	0.338096	-0.327893	-0.063296	-0.679146	-0.314873	0	0	1	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	1	0
11	-0.438422	-0.867672	-1.610141	0.716610	-0.891688	1.169781	-0.831155	-1.026167	-0.057788	0.246200	-0.490811	-1.078504	-0.877232	-0.426230	1.191438	-0.932014	-0.164511	0.155707	0.338096	0.325228	0.212802	-0.679146	1.086895	0	0	1	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	1
12	-0.438422	-0.648668	-0.328446	2.073946	-1.868426	-1.575686	-1.716872	0.379672	-0.961486	0.246200	-0.763209	-0.678049	0.489376	-0.426230	1.191438	0.241988	-0.807339	-1.396086	-1.077862	-0.327893	-0.615492	0.562576	-0.314873	0	0	1	1	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0
13	-0.438422	-0.320163	1.347427	1.210187	-0.891688	-0.660531	1.333932	0.379672	-0.961486	1.153254	-0.816328	-1.078504	-1.150554	-0.426230	0.266233	0.241988	-1.064470	-0.620189	0.338096	-0.817734	-0.615492	-0.368715	-0.595227	0	0	1	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	1	0
14	2.280906	-0.977174	-1.734097	1.827158	0.085049	0.254625	-0.781948	-1.026167	-0.961486	0.246200	-0.950827	0.923771	-0.330589	-0.426230	-0.658973	-0.932014	-0.678774	0.931603	0.338096	-0.491174	-0.615492	-0.679146	-0.314873	0	0	1	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1
15	-0.438422	-0.867672	1.454029	1.456975	1.061787	-0.660531	-0.732742	1.785511	0.845911	-1.567907	0.738803	-0.678049	-1.150554	-0.426230	0.266233	0.241988	-0.164511	-1.396086	0.338096	0.488508	1.317193	1.804297	1.086895	0	0	1	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	1	0
16	-0.438422	-0.539166	-1.161424	-0.517332	-0.891688	-1.575686	0.694247	1.785511	-0.961486	-0.660853	-0.680979	-1.078504	-0.877232	-0.426230	1.191438	1.415991	-0.550208	1.707500	-1.077862	-0.164613	-0.615492	-0.679146	0.245834	0	0	1	1	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1
17	-0.438422	-1.634185	0.794588	0.840004	-0.891688	1.169781	1.481551	1.785511	-0.961486	1.153254	-0.758109	-0.678049	-0.603911	-0.426230	-0.658973	1.415991	-1.321601	-0.620189	-1.077862	-0.981014	-1.167687	-0.679146	-1.155935	1	0	0	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	1
18	-0.438422	1.760373	1.032581	-0.887515	1.061787	-1.575686	0.595834	-1.026167	1.749610	1.153254	1.896174	-0.277594	0.216054	-0.426230	0.266233	-0.932014	2.535365	0.155707	0.338096	2.937711	1.041095	0.252146	0.806541	0	0	1	0	1	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0
19	-0.438422	0.117845	-1.069697	-0.887515	0.085049	1.169781	-1.027981	0.379672	-0.961486	1.153254	-0.543718	0.923771	-1.150554	-0.426230	0.266233	-0.932014	-0.678774	0.155707	0.338096	-0.654454	-0.615492	-0.368715	-0.595227	0	0	1	1	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1
20	-0.438422	-1.415181	-0.321008	0.223033	-0.891688	-1.575686	1.481551	1.785511	-0.057788	0.246200	-0.529482	-1.078504	0.762698	-0.426230	1.191438	0.241988	-0.807339	1.707500	-1.077862	-0.491174	-0.615492	-0.368715	-0.314873	1	0	0	1	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	1	0
21	2.280906	-0.101159	1.030102	-0.023755	1.061787	0.254625	0.792660	-1.026167	-0.961486	-1.567907	-0.657819	1.724681	2.129306	2.346151	-0.658973	-0.932014	-0.164511	0.931603	0.338096	-0.327893	-0.339394	-0.679146	-0.314873	0	0	1	0	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	1	0
22	-0.438422	-0.320163	-0.950700	-0.270544	1.061787	-1.575686	-0.634329	0.379672	0.845911	-0.660853	1.166735	-1.078504	-1.150554	-0.426230	0.266233	-0.932014	0.221185	0.931603	0.338096	0.815068	0.488900	-0.058285	1.927956	0	0	1	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	0
23	-0.438422	-1.743687	-1.020115	0.716610	-0.891688	0.254625	1.481551	0.379672	-0.961486	1.153254	-1.119960	-0.678049	-0.330589	-0.426230	1.191438	-0.932014	-1.450167	2.483396	0.338096	-1.144294	-1.167687	-0.679146	-1.155935	0	0	1	1	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	1	0
24	2.280906	-0.320163	-0.256552	-0.393938	-1.868426	-0.660531	0.841867	0.379672	-0.961486	-1.567907	-0.752797	-0.277594	-1.150554	-0.426230	0.266233	-0.932014	-0.421642	-0.620189	0.338096	-0.491174	-0.615492	-0.368715	-0.314873	0	0	1	1	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	1	0
25	-0.438422	1.760373	1.188765	-0.517332	0.085049	0.254625	-0.388296	0.379672	2.653309	0.246200	2.675333	0.523316	-1.150554	-0.426230	1.191438	0.241988	1.892537	0.155707	-1.077862	1.141629	2.421585	0.562576	1.086895	0	0	1	1	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	0
26	2.280906	-0.539166	0.799546	0.840004	-1.868426	-0.660531	0.300595	-2.432006	-0.961486	-1.567907	-0.549030	-0.678049	1.855984	2.346151	-0.658973	-0.932014	-0.164511	1.707500	0.338096	0.488508	-0.615492	1.183437	0.806541	0	1	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1
27	-0.438422	0.555852	-0.276384	-0.147150	1.061787	0.254625	-0.880361	0.379672	-0.057788	-0.660853	0.068433	-1.078504	-1.150554	-0.426230	1.191438	0.241988	-0.164511	-0.620189	0.338096	0.325228	0.764998	0.562576	-0.595227	0	0	1	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	1	0
28	-0.438422	0.774856	-0.806912	-0.270544	1.061787	-1.575686	-1.175601	-1.026167	0.845911	1.153254	0.795747	0.122861	-0.330589	-0.426230	1.191438	0.241988	1.635406	0.931603	0.338096	2.447870	0.488900	0.873006	3.610079	0	0	1	1	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0
29	-0.438422	0.993860	-0.241677	-0.887515	1.061787	-0.660531	0.841867	0.379672	2.653309	-1.567907	2.644099	0.122861	-0.877232	-0.426230	1.191438	-0.932014	1.378275	-0.620189	-1.077862	-0.817734	-0.615492	-0.058285	-0.875581	0	0	1	0	1	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	0
30	-0.438422	-0.429664	0.301246	-0.887515	0.085049	0.254625	0.595834	0.379672	-0.961486	1.153254	-0.851387	0.523316	-1.150554	-0.426230	1.191438	-0.932014	-0.550208	0.155707	0.338096	-0.981014	-0.891589	-0.679146	-1.155935	0	0	1	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	1	0
31	-0.438422	0.774856	1.627566	0.099639	1.061787	1.169781	-1.224807	0.379672	-0.057788	1.153254	-0.008060	-0.277594	-0.603911	-0.426230	1.191438	-0.932014	-0.293077	1.707500	1.754054	-0.491174	-0.615492	-0.368715	-0.314873	0	0	1	1	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1
32	-0.438422	-0.758170	-1.679556	-0.023755	-0.891688	1.169781	0.841867	-1.026167	-0.961486	0.246200	-0.913006	-0.678049	-0.603911	-0.426230	-1.584178	-0.932014	-0.164511	1.707500	0.338096	0.488508	-1.167687	-0.368715	1.086895	0	0	1	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0	0	0	0	1	1	0
33	2.280906	0.227347	0.229352	-0.517332	0.085049	1.169781	-0.486709	0.379672	-0.057788	1.153254	-0.938504	0.122861	-0.330589	-0.426230	0.266233	0.241988	0.992578	2.483396	1.754054	-0.981014	-1.167687	-0.679146	-1.155935	0	0	1	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	1	0	1	0	1	0
34	2.280906	-1.415181	0.026066	-1.010909	0.085049	-0.660531	-0.240677	0.379672	-0.961486	1.153254	-0.894521	-0.277594	0.216054	-0.426230	-1.584178	0.241988	-0.678774	-0.620189	-1.077862	-0.817734	-1.167687	-0.058285	-1.155935	0	0	1	1	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1
35	-0.438422	0.665354	1.166453	-0.887515	-0.891688	1.169781	0.300595	1.785511	-0.961486	0.246200	-0.819728	-0.678049	-0.877232	-0.426230	1.191438	1.415991	-0.678774	0.155707	-1.077862	-0.327893	-0.339394	-0.368715	-0.034520	0	0	1	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	1	0
36	2.280906	1.431867	0.164896	-0.764121	-0.891688	-1.575686	0.989486	-1.026167	-0.961486	0.246200	-0.811654	-0.678049	-0.330589	-0.426230	0.266233	-0.932014	-1.064470	-0.620189	0.338096	-0.654454	-0.615492	-0.679146	-0.595227	0	0	1	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1
37	-0.438422	-0.210661	0.216957	-0.887515	0.085049	1.169781	1.530758	0.379672	-0.961486	1.153254	-0.953802	-0.678049	-0.603911	-0.426230	-1.584178	-0.932014	-1.193036	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227	0	0	1	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	1	0
38	-0.438422	-0.101159	0.122751	-0.517332	1.061787	-0.660531	0.792660	-1.026167	-0.961486	-1.567907	-0.655270	2.525591	-0.330589	-0.426230	1.191438	0.241988	-0.678774	0.155707	1.754054	-0.981014	-0.891589	-0.679146	-1.155935	0	0	1	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1
39	-0.438422	-0.429664	0.839212	-1.010909	0.085049	0.254625	-1.175601	1.785511	-0.057788	-1.567907	-0.239449	-0.277594	1.036019	-0.426230	-1.584178	1.415991	-0.164511	0.155707	0.338096	-0.327893	-0.339394	-0.368715	-0.314873	0	1	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	0

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	-8.974e+15	1.03e+16	-0.870	0.384	-2.92e+16	1.13e+16
Attrition	55.3147	236.463	0.234	0.815	-408.638	519.268
Age	339.4669	263.853	1.287	0.199	-178.226	857.160
DailyRate	-198.3942	210.124	-0.944	0.345	-610.668	213.879
DistanceFromHome	324.1128	210.184	1.542	0.123	-88.279	736.504
Education	-274.3332	214.029	-1.282	0.200	-694.268	145.602
EnvironmentSatisfaction	500.0974	212.675	2.351	0.019	82.818	917.376
JobInvolvement	-227.9534	211.828	-1.076	0.282	-643.570	187.663
JobLevel	406.3416	755.408	0.538	0.591	-1075.807	1888.490
MonthlyIncome	-896.2061	869.313	-1.031	0.303	-2601.842	809.430
NumCompaniesWorked	68.0163	228.578	0.298	0.766	-380.465	516.498
PerformanceRating	-96.5287	215.409	-0.448	0.654	-519.172	326.115
StockOptionLevel	-188.4535	281.846	-0.669	0.504	-741.449	364.542
WorkLifeBalance	156.5437	209.514	0.747	0.455	-254.533	567.621
YearsAtCompany	-357.4432	399.247	-0.895	0.371	-1140.786	425.900
YearsSinceLastPromotion	160.8543	278.336	0.578	0.563	-385.255	706.963
YearsWithCurrManager	-339.4894	330.231	-1.028	0.304	-987.420	308.441
BusinessTravel_Non-Travel	3.785e+16	4.35e+16	0.870	0.384	-4.75e+16	1.23e+17
BusinessTravel_Travel_Frequently	3.785e+16	4.35e+16	0.870	0.384	-4.75e+16	1.23e+17
BusinessTravel_Travel_Rarely	3.785e+16	4.35e+16	0.870	0.384	-4.75e+16	1.23e+17
Department_Human Resources	5.879e+15	6.76e+15	0.870	0.384	-7.38e+15	1.91e+16
Department_Research & Development	5.879e+15	6.76e+15	0.870	0.384	-7.38e+15	1.91e+16
Department_Sales	5.879e+15	6.76e+15	0.870	0.384	-7.38e+15	1.91e+16
EducationField_Human Resources	-6.894e+15	7.92e+15	-0.870	0.384	-2.24e+16	8.65e+15
EducationField_Life Sciences	-6.894e+15	7.92e+15	-0.870	0.384	-2.24e+16	8.65e+15
EducationField_Marketing	-6.894e+15	7.92e+15	-0.870	0.384	-2.24e+16	8.65e+15
EducationField_Medical	-6.894e+15	7.92e+15	-0.870	0.384	-2.24e+16	8.65e+15
EducationField_Other	-6.894e+15	7.92e+15	-0.870	0.384	-2.24e+16	8.65e+15
EducationField_Technical Degree	-6.894e+15	7.92e+15	-0.870	0.384	-2.24e+16	8.65e+15
Gender_Female	1.512e+16	1.74e+16	0.870	0.384	-1.9e+16	4.92e+16
Gender_Male	1.512e+16	1.74e+16	0.870	0.384	-1.9e+16	4.92e+16
JobRole_Healthcare Representative	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15
JobRole_Human Resources	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15
JobRole_Laboratory Technician	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15
JobRole_Manager	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15
JobRole_Manufacturing Director	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15
JobRole_Research Director	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15
JobRole_Research Scientist	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15
JobRole_Sales Executive	-3.52e+15	4.05e+15	-0.870	0.384	-1.15e+16	4.42e+15

	coef	std err	t	P>\|t\|	[0.025	0.975]
const	-3.264e+16	2.68e+16	-1.217	0.224	-8.52e+16	2e+16
Age	352.6210	254.561	1.385	0.166	-146.840	852.082
DailyRate	-200.5853	209.525	-0.957	0.339	-611.683	210.513
DistanceFromHome	333.9129	209.252	1.596	0.111	-76.649	744.475
Education	-251.5467	212.235	-1.185	0.236	-667.963	164.869
EnvironmentSatisfaction	479.3843	210.233	2.280	0.023	66.898	891.871
JobInvolvement	-233.9587	209.515	-1.117	0.264	-645.037	177.120
JobLevel	409.6049	754.528	0.543	0.587	-1070.815	1890.025
MonthlyIncome	-901.9764	865.201	-1.043	0.297	-2599.542	795.589
PerformanceRating	-106.3710	215.392	-0.494	0.622	-528.980	316.238
StockOptionLevel	-178.4928	281.507	-0.634	0.526	-730.823	373.837
WorkLifeBalance	139.8863	209.367	0.668	0.504	-270.901	550.674
YearsAtCompany	-388.7480	391.483	-0.993	0.321	-1156.856	379.360
YearsSinceLastPromotion	160.2822	276.259	0.580	0.562	-381.750	702.314
YearsWithCurrManager	-338.9006	328.951	-1.030	0.303	-984.319	306.517
BusinessTravel_Non-Travel	2.373e+16	1.95e+16	1.217	0.224	-1.45e+16	6.2e+16
BusinessTravel_Travel_Frequently	2.373e+16	1.95e+16	1.217	0.224	-1.45e+16	6.2e+16
BusinessTravel_Travel_Rarely	2.373e+16	1.95e+16	1.217	0.224	-1.45e+16	6.2e+16
Department_Human Resources	1.32e+16	1.08e+16	1.217	0.224	-8.08e+15	3.45e+16
Department_Research & Development	1.32e+16	1.08e+16	1.217	0.224	-8.08e+15	3.45e+16
Department_Sales	1.32e+16	1.08e+16	1.217	0.224	-8.08e+15	3.45e+16
EducationField_Human Resources	-9.851e+15	8.09e+15	-1.217	0.224	-2.57e+16	6.03e+15
EducationField_Life Sciences	-9.851e+15	8.09e+15	-1.217	0.224	-2.57e+16	6.03e+15
EducationField_Marketing	-9.851e+15	8.09e+15	-1.217	0.224	-2.57e+16	6.03e+15
EducationField_Medical	-9.851e+15	8.09e+15	-1.217	0.224	-2.57e+16	6.03e+15
EducationField_Other	-9.851e+15	8.09e+15	-1.217	0.224	-2.57e+16	6.03e+15
EducationField_Technical Degree	-9.851e+15	8.09e+15	-1.217	0.224	-2.57e+16	6.03e+15
Gender_Female	-2.472e+15	2.03e+15	-1.217	0.224	-6.46e+15	1.51e+15
Gender_Male	-2.472e+15	2.03e+15	-1.217	0.224	-6.46e+15	1.51e+15
JobRole_Healthcare Representative	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Human Resources	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Laboratory Technician	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Manager	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Manufacturing Director	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Research Director	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Research Scientist	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Sales Executive	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
JobRole_Sales Representative	-5.806e+15	4.77e+15	-1.217	0.224	-1.52e+16	3.55e+15
MaritalStatus_Divorced	-2.374e+16	1.95e+16	-1.217	0.224	-6.2e+16	1.45e+16

	MonthlyIncome	HourlyRate
0	-0.108350	1.383138
1	-0.291719	-0.240677
2	-0.937654	1.284725
3	-0.763634	-0.486709
4	-0.644858	-1.274014
5	-0.729850	0.645041
6	-0.814416	0.743454
7	-0.809529	0.054562
8	0.642338	-1.077188
9	-0.268983	1.383138
10	-0.866261	0.891073
11	-0.490811	-0.831155
12	-0.763209	-1.716872
13	-0.816328	1.333932
14	-0.950827	-0.781948
15	0.738803	-0.732742
16	-0.680979	0.694247
17	-0.758109	1.481551
18	1.896174	0.595834
19	-0.543718	-1.027981
20	-0.529482	1.481551
21	-0.657819	0.792660
22	1.166735	-0.634329
23	-1.119960	1.481551
24	-0.752797	0.841867
25	2.675333	-0.388296
26	-0.549030	0.300595
27	0.068433	-0.880361
28	0.795747	-1.175601
29	2.644099	0.841867
30	-0.851387	0.595834
31	-0.008060	-1.224807
32	-0.913006	0.841867
33	-0.938504	-0.486709
34	-0.894521	-0.240677
35	-0.819728	0.300595
36	-0.811654	0.989486
37	-0.953802	1.530758
38	-0.655270	0.792660
39	-0.239449	-1.175601

We can see data need not to replace any value but still Replacer and Preprocessing function we will create¶

Replacer¶

Preprocessing¶

Exploratory Data Analysis¶

Univariate Analysis¶

Pair PLot is little bit time consuming¶

Define X and Y¶

Bivariate Analysis¶

Preprocessing¶

OLS¶

Anova Creation¶

Anova Of Predictors¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Anova Of Predictors For Cat vs Con¶

Divide Data into training and testing set¶

Linear Regresssion¶

Lasso¶

Ridge¶

Decision Tree Regressor¶

Random Forest Regressor¶

Adaboost Regressor¶

K Nearest Neighbors¶

XGBooost¶

Naive Bayes Alogrithm¶

Keras Tuner¶

Neural Network For MonthlyRate Loss and Val Loass¶

Now From Here Our Aim Is To Check the Attrition of Employees¶

Replacer¶

Define X and Y¶

Now Y ----> Attrition¶

Adaboost¶

Dtree¶

RfC¶

Artificial Neural Network¶

Keras Tuner¶

Dense Layers¶

Dropout Layers¶

xgboost Alog¶

Naive Bayes Alog¶

Logistic Regression¶

Clustering¶

Hierarichal Clustering¶